Upload 6 files

checkpoints/log_YGD_gesture_seg_2spk/config.yaml

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+## Config file
+
+# Log 
+seed: 777
+use_cuda: 1           # 1 for True, 0 for False
+
+# dataset
+speaker_no: 2
+mix_lst_path: ./data/YGD/mixture_data_list_2mix.csv
+audio_direc: /mnt/nas_sg/wulanchabu/zexu.pan/datasets/gesture_TED/audio_clean/
+reference_direc: /mnt/nas_sg/wulanchabu/zexu.pan/datasets/gesture_TED/visual/gesture_embedding/
+audio_sr: 16000
+ref_sr: 15
+
+# dataloader
+num_workers: 4
+batch_size: 8
+accu_grad: 1
+effec_batch_size: 16   # per GPU, only used if accu_grad is set to 1, must be multiple times of batch size
+max_length: 10         # truncate the utterances in dataloader, in seconds 
+
+# network settings
+init_from: None       # 'None' or a log name 'log_2024-07-22(18:12:13)'
+causal: 0             # 1 for True, 0 for False
+network_reference:
+  cue: gesture            # lip or speech or gesture or EEG
+network_audio:
+  backbone: seg
+  N: 256
+  L: 40
+  B: 64
+  H: 128
+  K: 100
+  R: 6
+
+# optimizer
+loss_type: sisdr      # "snr", "sisdr", "hybrid"
+init_learning_rate: 0.0005
+max_epoch: 200
+clip_grad_norm: 5

checkpoints/log_YGD_gesture_seg_2spk/last_best_checkpoint.pt

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+size 53037174

checkpoints/log_YGD_gesture_seg_2spk/last_checkpoint.pt

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+size 53037174

checkpoints/log_YGD_gesture_seg_2spk/log_2024-09-26(16:20:37).txt

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+## Config file
+
+# Log 
+seed: 777
+use_cuda: 1           # 1 for True, 0 for False
+
+# dataset
+speaker_no: 2
+mix_lst_path: ./data/YGD/mixture_data_list_2mix.csv
+audio_direc: /mnt/nas_sg/mit_sg/zexu.pan/datasets/gesture_TED/audio_clean/
+reference_direc: /mnt/nas_sg/mit_sg/zexu.pan/datasets/gesture_TED/visual/gesture_embedding/
+audio_sr: 16000
+visual_sr: 15
+
+# dataloader
+num_workers: 4
+batch_size: 8
+accu_grad: 1
+effec_batch_size: 16   # per GPU, only used if accu_grad is set to 1, must be multiple times of batch size
+max_length: 10         # truncate the utterances in dataloader, in seconds 
+
+# network settings
+init_from: None       # 'None' or a log name 'log_2024-07-22(18:12:13)'
+causal: 0             # 1 for True, 0 for False
+network_reference:
+  cue: gesture            # lip or speech or gesture or EEG
+network_audio:
+  backbone: seg
+  N: 256
+  L: 40
+  B: 64
+  H: 128
+  K: 100
+  R: 6
+
+# optimizer
+loss_type: sisdr      # "snr", "sisdr", "hybrid"
+init_learning_rate: 0.0005
+max_epoch: 200
+clip_grad_norm: 5
+W0926 16:20:40.020566 139890934855488 torch/distributed/run.py:757] 
+W0926 16:20:40.020566 139890934855488 torch/distributed/run.py:757] *****************************************
+W0926 16:20:40.020566 139890934855488 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0926 16:20:40.020566 139890934855488 torch/distributed/run.py:757] *****************************************
+started on checkpoints/log_2024-09-26(16:20:37)
+
+namespace(seed=777, use_cuda=1, config=[<yamlargparse.Path object at 0x7f4635886e80>], checkpoint_dir='checkpoints/log_2024-09-26(16:20:37)', train_from_last_checkpoint=0, loss_type='sisdr', init_learning_rate=0.0005, max_epoch=200, clip_grad_norm=5.0, batch_size=8, accu_grad=1, effec_batch_size=16, max_length=10, num_workers=4, causal=0, network_reference=namespace(cue='gesture'), network_audio=namespace(backbone='seg', N=256, L=40, B=64, H=128, K=100, R=6), init_from='None', mix_lst_path='./data/YGD/mixture_data_list_2mix.csv', audio_direc='/mnt/nas_sg/mit_sg/zexu.pan/datasets/gesture_TED/audio_clean/', reference_direc='/mnt/nas_sg/mit_sg/zexu.pan/datasets/gesture_TED/visual/gesture_embedding/', speaker_no=2, audio_sr=16000, visual_sr=15, local_rank=0, distributed=True, world_size=2, device=device(type='cuda'))
+network_wrapper(
+  (sep_network): seg(
+    (encoder): Encoder(
+      (conv1d_U): Conv1d(1, 256, kernel_size=(40,), stride=(20,), bias=False)
+    )
+    (separator): rnn(
+      (layer_norm): GroupNorm(1, 256, eps=1e-08, affine=True)
+      (bottleneck_conv1x1): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False)
+      (dual_rnn): ModuleList(
+        (0-5): 6 x Dual_RNN_Block(
+          (intra_rnn): LSTM(64, 128, batch_first=True, bidirectional=True)
+          (inter_rnn): LSTM(64, 128, batch_first=True, bidirectional=True)
+          (intra_norm): GroupNorm(1, 64, eps=1e-08, affine=True)
+          (inter_norm): GroupNorm(1, 64, eps=1e-08, affine=True)
+          (intra_linear): Linear(in_features=256, out_features=64, bias=True)
+          (inter_linear): Linear(in_features=256, out_features=64, bias=True)
+        )
+      )
+      (prelu): PReLU(num_parameters=1)
+      (mask_conv1x1): Conv1d(64, 256, kernel_size=(1,), stride=(1,), bias=False)
+      (visual_net): LSTM(30, 128, num_layers=5, batch_first=True, dropout=0.3, bidirectional=True)
+      (av_conv): Conv1d(320, 64, kernel_size=(1,), stride=(1,), bias=False)
+    )
+    (decoder): Decoder(
+      (basis_signals): Linear(in_features=256, out_features=40, bias=False)
+    )
+  )
+)
+
+Total number of parameters: 4401921 
+
+
+Total number of trainable parameters: 4401921 
+
+Start new training from scratch
+[rank1]:[W reducer.cpp:1389] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
+[rank0]:[W reducer.cpp:1389] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
+Train Summary | End of Epoch 1 | Time 5588.81s | Train Loss -1.305
+Valid Summary | End of Epoch 1 | Time 102.37s | Valid Loss -2.629
+Test Summary | End of Epoch 1 | Time 79.44s | Test Loss -2.629
+Fund new best model, dict saved
+Train Summary | End of Epoch 2 | Time 5433.42s | Train Loss -3.538
+Valid Summary | End of Epoch 2 | Time 34.91s | Valid Loss -4.547
+Test Summary | End of Epoch 2 | Time 22.02s | Test Loss -4.547
+Fund new best model, dict saved
+Train Summary | End of Epoch 3 | Time 5432.23s | Train Loss -5.161
+Valid Summary | End of Epoch 3 | Time 33.92s | Valid Loss -5.681
+Test Summary | End of Epoch 3 | Time 24.24s | Test Loss -5.681
+Fund new best model, dict saved
+Train Summary | End of Epoch 4 | Time 5428.65s | Train Loss -6.261
+Valid Summary | End of Epoch 4 | Time 39.14s | Valid Loss -6.456
+Test Summary | End of Epoch 4 | Time 21.84s | Test Loss -6.456
+Fund new best model, dict saved
+Train Summary | End of Epoch 5 | Time 5438.49s | Train Loss -7.072
+Valid Summary | End of Epoch 5 | Time 35.85s | Valid Loss -7.183
+Test Summary | End of Epoch 5 | Time 21.63s | Test Loss -7.183
+Fund new best model, dict saved
+Train Summary | End of Epoch 6 | Time 5437.45s | Train Loss -7.711
+Valid Summary | End of Epoch 6 | Time 37.30s | Valid Loss -7.557
+Test Summary | End of Epoch 6 | Time 21.09s | Test Loss -7.557
+Fund new best model, dict saved
+Train Summary | End of Epoch 7 | Time 5444.24s | Train Loss -8.252
+Valid Summary | End of Epoch 7 | Time 39.37s | Valid Loss -7.818
+Test Summary | End of Epoch 7 | Time 22.48s | Test Loss -7.818
+Fund new best model, dict saved
+Train Summary | End of Epoch 8 | Time 5445.25s | Train Loss -8.708
+Valid Summary | End of Epoch 8 | Time 38.85s | Valid Loss -8.094
+Test Summary | End of Epoch 8 | Time 21.59s | Test Loss -8.094
+Fund new best model, dict saved
+Train Summary | End of Epoch 9 | Time 5440.56s | Train Loss -9.104
+Valid Summary | End of Epoch 9 | Time 39.05s | Valid Loss -8.193
+Test Summary | End of Epoch 9 | Time 21.58s | Test Loss -8.193
+Fund new best model, dict saved
+Train Summary | End of Epoch 10 | Time 5441.83s | Train Loss -9.437
+Valid Summary | End of Epoch 10 | Time 40.08s | Valid Loss -8.662
+Test Summary | End of Epoch 10 | Time 21.44s | Test Loss -8.662
+Fund new best model, dict saved
+Train Summary | End of Epoch 11 | Time 5441.18s | Train Loss -9.732
+Valid Summary | End of Epoch 11 | Time 39.35s | Valid Loss -8.394
+Test Summary | End of Epoch 11 | Time 21.36s | Test Loss -8.394
+Train Summary | End of Epoch 12 | Time 5444.51s | Train Loss -9.903
+Valid Summary | End of Epoch 12 | Time 40.10s | Valid Loss -8.676
+Test Summary | End of Epoch 12 | Time 21.38s | Test Loss -8.676
+Fund new best model, dict saved
+Train Summary | End of Epoch 13 | Time 5441.35s | Train Loss -10.155
+Valid Summary | End of Epoch 13 | Time 39.96s | Valid Loss -8.511
+Test Summary | End of Epoch 13 | Time 21.63s | Test Loss -8.511
+Train Summary | End of Epoch 14 | Time 5455.11s | Train Loss -10.352
+Valid Summary | End of Epoch 14 | Time 41.14s | Valid Loss -8.700
+Test Summary | End of Epoch 14 | Time 22.14s | Test Loss -8.700
+Fund new best model, dict saved
+Train Summary | End of Epoch 15 | Time 5440.99s | Train Loss -10.585
+Valid Summary | End of Epoch 15 | Time 43.36s | Valid Loss -8.711
+Test Summary | End of Epoch 15 | Time 21.50s | Test Loss -8.711
+Fund new best model, dict saved
+Train Summary | End of Epoch 16 | Time 5456.88s | Train Loss -10.789
+Valid Summary | End of Epoch 16 | Time 43.19s | Valid Loss -8.695
+Test Summary | End of Epoch 16 | Time 21.59s | Test Loss -8.695
+Train Summary | End of Epoch 17 | Time 5443.70s | Train Loss -10.967
+Valid Summary | End of Epoch 17 | Time 43.61s | Valid Loss -9.081
+Test Summary | End of Epoch 17 | Time 21.51s | Test Loss -9.081
+Fund new best model, dict saved
+Train Summary | End of Epoch 18 | Time 5459.34s | Train Loss -11.180
+Valid Summary | End of Epoch 18 | Time 43.24s | Valid Loss -8.954
+Test Summary | End of Epoch 18 | Time 21.62s | Test Loss -8.954
+Train Summary | End of Epoch 19 | Time 5447.97s | Train Loss -11.329
+Valid Summary | End of Epoch 19 | Time 44.06s | Valid Loss -8.925
+Test Summary | End of Epoch 19 | Time 21.67s | Test Loss -8.925
+Train Summary | End of Epoch 20 | Time 5458.93s | Train Loss -11.507
+Valid Summary | End of Epoch 20 | Time 41.99s | Valid Loss -9.317
+Test Summary | End of Epoch 20 | Time 22.73s | Test Loss -9.317
+Fund new best model, dict saved
+Train Summary | End of Epoch 21 | Time 5447.55s | Train Loss -11.624
+Valid Summary | End of Epoch 21 | Time 43.85s | Valid Loss -9.092
+Test Summary | End of Epoch 21 | Time 21.68s | Test Loss -9.092
+Train Summary | End of Epoch 22 | Time 5461.35s | Train Loss -11.787
+Valid Summary | End of Epoch 22 | Time 42.36s | Valid Loss -9.352
+Test Summary | End of Epoch 22 | Time 22.75s | Test Loss -9.352
+Fund new best model, dict saved
+Train Summary | End of Epoch 23 | Time 5449.89s | Train Loss -11.941
+Valid Summary | End of Epoch 23 | Time 42.75s | Valid Loss -9.264
+Test Summary | End of Epoch 23 | Time 21.85s | Test Loss -9.264
+Train Summary | End of Epoch 24 | Time 5453.22s | Train Loss -12.123
+Valid Summary | End of Epoch 24 | Time 41.88s | Valid Loss -9.722
+Test Summary | End of Epoch 24 | Time 21.71s | Test Loss -9.722
+Fund new best model, dict saved
+Train Summary | End of Epoch 25 | Time 5431.43s | Train Loss -12.248
+Valid Summary | End of Epoch 25 | Time 42.21s | Valid Loss -9.276
+Test Summary | End of Epoch 25 | Time 21.50s | Test Loss -9.276
+Train Summary | End of Epoch 26 | Time 5433.43s | Train Loss -12.358
+Valid Summary | End of Epoch 26 | Time 41.90s | Valid Loss -9.327
+Test Summary | End of Epoch 26 | Time 21.22s | Test Loss -9.327
+Train Summary | End of Epoch 27 | Time 5432.86s | Train Loss -12.511
+Valid Summary | End of Epoch 27 | Time 43.27s | Valid Loss -9.592
+Test Summary | End of Epoch 27 | Time 21.57s | Test Loss -9.592
+Train Summary | End of Epoch 28 | Time 5433.50s | Train Loss -12.606
+Valid Summary | End of Epoch 28 | Time 42.16s | Valid Loss -9.127
+Test Summary | End of Epoch 28 | Time 21.75s | Test Loss -9.127
+Train Summary | End of Epoch 29 | Time 5432.61s | Train Loss -12.745
+Valid Summary | End of Epoch 29 | Time 42.15s | Valid Loss -9.458
+Test Summary | End of Epoch 29 | Time 21.61s | Test Loss -9.458
+reload weights and optimizer from last best checkpoint
+Learning rate adjusted to: 0.000250
+Train Summary | End of Epoch 30 | Time 5436.75s | Train Loss -13.005
+Valid Summary | End of Epoch 30 | Time 40.19s | Valid Loss -9.828
+Test Summary | End of Epoch 30 | Time 21.68s | Test Loss -9.828
+Fund new best model, dict saved
+Train Summary | End of Epoch 31 | Time 5432.80s | Train Loss -13.319
+Valid Summary | End of Epoch 31 | Time 40.07s | Valid Loss -9.674
+Test Summary | End of Epoch 31 | Time 21.69s | Test Loss -9.674
+Train Summary | End of Epoch 32 | Time 5432.97s | Train Loss -13.489
+Valid Summary | End of Epoch 32 | Time 39.89s | Valid Loss -9.631
+Test Summary | End of Epoch 32 | Time 21.98s | Test Loss -9.631
+Train Summary | End of Epoch 33 | Time 5428.97s | Train Loss -13.637
+Valid Summary | End of Epoch 33 | Time 41.71s | Valid Loss -9.938
+Test Summary | End of Epoch 33 | Time 21.12s | Test Loss -9.938
+Fund new best model, dict saved
+Train Summary | End of Epoch 34 | Time 5431.64s | Train Loss -13.739
+Valid Summary | End of Epoch 34 | Time 39.77s | Valid Loss -9.937
+Test Summary | End of Epoch 34 | Time 21.39s | Test Loss -9.937
+Train Summary | End of Epoch 35 | Time 5429.93s | Train Loss -13.865
+Valid Summary | End of Epoch 35 | Time 39.59s | Valid Loss -9.834
+Test Summary | End of Epoch 35 | Time 21.54s | Test Loss -9.834
+Train Summary | End of Epoch 36 | Time 5428.18s | Train Loss -13.960
+Valid Summary | End of Epoch 36 | Time 39.94s | Valid Loss -9.921
+Test Summary | End of Epoch 36 | Time 21.84s | Test Loss -9.921
+Train Summary | End of Epoch 37 | Time 5428.69s | Train Loss -14.041
+Valid Summary | End of Epoch 37 | Time 39.93s | Valid Loss -9.942
+Test Summary | End of Epoch 37 | Time 21.24s | Test Loss -9.942
+Fund new best model, dict saved
+Train Summary | End of Epoch 38 | Time 5422.98s | Train Loss -14.130
+Valid Summary | End of Epoch 38 | Time 42.71s | Valid Loss -10.054
+Test Summary | End of Epoch 38 | Time 21.17s | Test Loss -10.054
+Fund new best model, dict saved
+Train Summary | End of Epoch 39 | Time 5423.27s | Train Loss -14.214
+Valid Summary | End of Epoch 39 | Time 42.28s | Valid Loss -9.860
+Test Summary | End of Epoch 39 | Time 21.14s | Test Loss -9.860
+Train Summary | End of Epoch 40 | Time 5422.16s | Train Loss -14.298
+Valid Summary | End of Epoch 40 | Time 42.51s | Valid Loss -10.045
+Test Summary | End of Epoch 40 | Time 21.07s | Test Loss -10.045
+Train Summary | End of Epoch 41 | Time 5421.24s | Train Loss -14.363
+Valid Summary | End of Epoch 41 | Time 43.04s | Valid Loss -9.873
+Test Summary | End of Epoch 41 | Time 21.63s | Test Loss -9.873
+Train Summary | End of Epoch 42 | Time 5421.36s | Train Loss -14.421
+Valid Summary | End of Epoch 42 | Time 42.79s | Valid Loss -9.767
+Test Summary | End of Epoch 42 | Time 21.35s | Test Loss -9.767
+Train Summary | End of Epoch 43 | Time 5422.27s | Train Loss -14.489
+Valid Summary | End of Epoch 43 | Time 43.02s | Valid Loss -9.945
+Test Summary | End of Epoch 43 | Time 22.01s | Test Loss -9.945
+reload weights and optimizer from last best checkpoint
+Learning rate adjusted to: 0.000125
+Train Summary | End of Epoch 44 | Time 5422.77s | Train Loss -14.518
+Valid Summary | End of Epoch 44 | Time 44.66s | Valid Loss -9.955
+Test Summary | End of Epoch 44 | Time 21.15s | Test Loss -9.955
+Train Summary | End of Epoch 45 | Time 5422.81s | Train Loss -14.641
+Valid Summary | End of Epoch 45 | Time 42.37s | Valid Loss -10.015
+Test Summary | End of Epoch 45 | Time 21.51s | Test Loss -10.015
+Train Summary | End of Epoch 46 | Time 5424.07s | Train Loss -14.713
+Valid Summary | End of Epoch 46 | Time 42.38s | Valid Loss -10.161
+Test Summary | End of Epoch 46 | Time 21.26s | Test Loss -10.161
+Fund new best model, dict saved
+Train Summary | End of Epoch 47 | Time 5421.37s | Train Loss -14.775
+Valid Summary | End of Epoch 47 | Time 44.70s | Valid Loss -10.079
+Test Summary | End of Epoch 47 | Time 21.16s | Test Loss -10.079
+Train Summary | End of Epoch 48 | Time 5421.69s | Train Loss -14.832
+Valid Summary | End of Epoch 48 | Time 43.73s | Valid Loss -9.943
+Test Summary | End of Epoch 48 | Time 21.23s | Test Loss -9.943
+Train Summary | End of Epoch 49 | Time 5421.44s | Train Loss -14.864
+Valid Summary | End of Epoch 49 | Time 44.25s | Valid Loss -10.162
+Test Summary | End of Epoch 49 | Time 21.19s | Test Loss -10.162
+Fund new best model, dict saved
+Train Summary | End of Epoch 50 | Time 5423.12s | Train Loss -14.920
+Valid Summary | End of Epoch 50 | Time 43.14s | Valid Loss -9.900
+Test Summary | End of Epoch 50 | Time 21.28s | Test Loss -9.900
+Train Summary | End of Epoch 51 | Time 5419.93s | Train Loss -14.957
+Valid Summary | End of Epoch 51 | Time 45.30s | Valid Loss -9.932
+Test Summary | End of Epoch 51 | Time 21.22s | Test Loss -9.932
+Train Summary | End of Epoch 52 | Time 5422.40s | Train Loss -14.995
+Valid Summary | End of Epoch 52 | Time 43.00s | Valid Loss -10.064
+Test Summary | End of Epoch 52 | Time 21.27s | Test Loss -10.064
+Train Summary | End of Epoch 53 | Time 5422.21s | Train Loss -15.036
+Valid Summary | End of Epoch 53 | Time 43.88s | Valid Loss -10.209
+Test Summary | End of Epoch 53 | Time 20.97s | Test Loss -10.209
+Fund new best model, dict saved
+Train Summary | End of Epoch 54 | Time 5419.59s | Train Loss -15.065
+Valid Summary | End of Epoch 54 | Time 45.94s | Valid Loss -10.001
+Test Summary | End of Epoch 54 | Time 21.08s | Test Loss -10.001
+Train Summary | End of Epoch 55 | Time 5418.47s | Train Loss -15.095
+Valid Summary | End of Epoch 55 | Time 45.19s | Valid Loss -10.026
+Test Summary | End of Epoch 55 | Time 21.20s | Test Loss -10.026
+Train Summary | End of Epoch 56 | Time 5417.08s | Train Loss -15.141
+Valid Summary | End of Epoch 56 | Time 45.64s | Valid Loss -9.933
+Test Summary | End of Epoch 56 | Time 21.08s | Test Loss -9.933
+Train Summary | End of Epoch 57 | Time 5417.95s | Train Loss -15.165
+Valid Summary | End of Epoch 57 | Time 45.28s | Valid Loss -9.704
+Test Summary | End of Epoch 57 | Time 21.29s | Test Loss -9.704
+Train Summary | End of Epoch 58 | Time 5418.70s | Train Loss -15.196
+Valid Summary | End of Epoch 58 | Time 44.81s | Valid Loss -10.054
+Test Summary | End of Epoch 58 | Time 21.03s | Test Loss -10.054
+reload weights and optimizer from last best checkpoint
+Learning rate adjusted to: 0.000063
+Train Summary | End of Epoch 59 | Time 5420.00s | Train Loss -15.200
+Valid Summary | End of Epoch 59 | Time 46.30s | Valid Loss -9.865
+Test Summary | End of Epoch 59 | Time 21.27s | Test Loss -9.865
+Train Summary | End of Epoch 60 | Time 5420.50s | Train Loss -15.248
+Valid Summary | End of Epoch 60 | Time 46.35s | Valid Loss -10.050
+Test Summary | End of Epoch 60 | Time 21.29s | Test Loss -10.050
+Train Summary | End of Epoch 61 | Time 5420.45s | Train Loss -15.277
+Valid Summary | End of Epoch 61 | Time 47.57s | Valid Loss -10.149
+Test Summary | End of Epoch 61 | Time 21.66s | Test Loss -10.149
+Train Summary | End of Epoch 62 | Time 5419.90s | Train Loss -15.303
+Valid Summary | End of Epoch 62 | Time 46.20s | Valid Loss -10.096
+Test Summary | End of Epoch 62 | Time 21.31s | Test Loss -10.096
+Start evaluation
+Avg SISNR:i tensor([9.4806], device='cuda:0')
+Avg SNRi: 10.412415402130565
+Avg STOIi: 0.11529820576481561

checkpoints/log_YGD_gesture_seg_2spk/tensorboard/events.out.tfevents.1727338847.bach-gpu011017044238.na61.87202.0

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+oid sha256:22936579a6cea616d3d368d296f82fdd01d5b418f83a23cc873106efb7f7ad49
+size 9264