added wavlm

partly_frozen_splitted_wavlm/1986/app.py

@@ -0,0 +1,371 @@
+import os
+import sys
+import torch
+import logging
+import speechbrain as sb
+from speechbrain.utils.distributed import run_on_main
+from hyperpyyaml import load_hyperpyyaml
+from pathlib import Path
+import torchaudio.transforms as T
+import torchaudio
+import numpy as np
+
+from pyctcdecode import build_ctcdecoder
+hparams_file, run_opts, overrides = sb.parse_arguments(["wavlm_partly_frozen.yaml"])
+
+# If distributed_launch=True then
+# create ddp_group with the right communication protocol
+sb.utils.distributed.ddp_init_group(run_opts)
+
+with open(hparams_file) as fin:
+    hparams = load_hyperpyyaml(fin, overrides)
+
+# Create experiment directory
+sb.create_experiment_directory(
+    experiment_directory=hparams["output_folder"],
+    hyperparams_to_save=hparams_file,
+    overrides=overrides,
+)
+def read_labels_file(labels_file): 
+    with open(labels_file, "r") as lf: 
+        lines = lf.read().splitlines()
+        division = "==="
+        numbers = {}
+        for line in lines : 
+            if division in line : 
+                break
+            string, number = line.split("=>")
+            number = int(number)
+            string = string[1:-2]
+            numbers[number] = string
+        return [numbers[x] for x in range(len(numbers))]
+labels = read_labels_file(os.path.join(hparams["save_folder"], "label_encoder.txt"))
+print(labels)
+labels = [""] + labels[1:]
+print(len(labels))
+
+# Dataset prep (parsing Librispeech)
+
+resampler_8000  = T.Resample(8000, 16000, dtype=torch.float)
+
+resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
+resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
+
+
+resamplers = {"8000": resampler_8000, "44100":resampler_44100, "48000": resampler_48000}
+def dataio_prepare(hparams):
+    """This function prepares the datasets to be used in the brain class.
+    It also defines the data processing pipeline through user-defined functions."""
+    data_folder = hparams["data_folder"]
+
+    train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
+        csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
+    )
+
+    if hparams["sorting"] == "ascending":
+        # we sort training data to speed up training and get better results.
+        train_data = train_data.filtered_sorted(sort_key="duration")
+        # when sorting do not shuffle in dataloader ! otherwise is pointless
+        hparams["train_dataloader_opts"]["shuffle"] = False
+
+    elif hparams["sorting"] == "descending":
+        train_data = train_data.filtered_sorted(
+            sort_key="duration", reverse=True
+        )
+        # when sorting do not shuffle in dataloader ! otherwise is pointless
+        hparams["train_dataloader_opts"]["shuffle"] = False
+
+    elif hparams["sorting"] == "random":
+        pass
+
+    else:
+        raise NotImplementedError(
+            "sorting must be random, ascending or descending"
+        )
+
+    valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
+        csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
+    )
+    valid_data = valid_data.filtered_sorted(sort_key="duration")
+
+    # test is separate
+    test_datasets = {}
+    for csv_file in hparams["test_csv"]:
+        name = Path(csv_file).stem
+        test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
+            csv_path=csv_file, replacements={"data_root": data_folder}
+        )
+        test_datasets[name] = test_datasets[name].filtered_sorted(
+            sort_key="duration"
+        )
+
+    datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
+
+    # 2. Define audio pipeline:
+    @sb.utils.data_pipeline.takes("wav", "sr")
+    @sb.utils.data_pipeline.provides("sig")
+    def audio_pipeline(wav, sr):
+        sig = sb.dataio.dataio.read_audio(wav)
+        sig = resamplers[sr](sig)
+        return sig
+
+    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
+    label_encoder = sb.dataio.encoder.CTCTextEncoder()
+
+    # 3. Define text pipeline:
+    @sb.utils.data_pipeline.takes("wrd")
+    @sb.utils.data_pipeline.provides(
+        "wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
+    )
+    def text_pipeline(wrd):
+        yield wrd
+        char_list = list(wrd)
+        yield char_list
+        tokens_list = label_encoder.encode_sequence(char_list)
+        yield tokens_list
+        tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
+        yield tokens_bos
+        tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
+        yield tokens_eos
+        tokens = torch.LongTensor(tokens_list)
+        yield tokens
+
+    sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
+
+    lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
+    special_labels = {
+        "bos_label": hparams["bos_index"],
+        "eos_label": hparams["eos_index"],
+        "blank_label": hparams["blank_index"],
+    }
+    label_encoder.load_or_create(
+        path=lab_enc_file,
+        from_didatasets=[train_data],
+        output_key="char_list",
+        special_labels=special_labels,
+        sequence_input=True,
+    )
+
+    # 4. Set output:
+    sb.dataio.dataset.set_output_keys(
+        datasets,
+        ["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
+    )
+    return train_data, valid_data, test_datasets, label_encoder
+
+
+class ASR(sb.Brain):
+    def compute_forward(self, batch, stage):
+        """Forward computations from the waveform batches to the output probabilities."""
+        batch = batch.to(self.device)
+        wavs, wav_lens = batch.sig
+        print(wavs)
+        tokens_bos, _ = batch.tokens_bos
+        wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
+
+        # Forward pass
+        feats = self.modules.wav2vec2(wavs)
+        x = self.modules.enc(feats)
+        # Compute outputs
+        p_tokens = None
+        logits = self.modules.ctc_lin(x)
+        p_ctc = self.hparams.log_softmax(logits)
+        if stage != sb.Stage.TRAIN:
+            p_tokens = sb.decoders.ctc_greedy_decode(
+                p_ctc, wav_lens, blank_id=self.hparams.blank_index
+            )
+        return p_ctc, wav_lens, p_tokens
+
+    def treat_wav(self,sig):
+        feats = self.modules.wav2vec2(sig.to(self.device))
+        x = self.modules.enc(feats)
+        p_tokens = None
+        logits = self.modules.ctc_lin(x)
+        p_ctc = self.hparams.log_softmax(logits)
+        predicted_words =[]
+        for logs in p_ctc:
+            text = decoder.decode(logs.detach().cpu().numpy())
+            predicted_words.append(text.split(" "))
+        return " ".join(predicted_words[0])
+
+
+
+
+    def compute_objectives(self, predictions, batch, stage):
+        """Computes the loss (CTC+NLL) given predictions and targets."""
+
+        p_ctc, wav_lens, predicted_tokens = predictions
+
+        ids = batch.id
+        tokens_eos, tokens_eos_lens = batch.tokens_eos
+        tokens, tokens_lens = batch.tokens
+
+        if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
+            tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
+            tokens_eos_lens = torch.cat(
+                [tokens_eos_lens, tokens_eos_lens], dim=0
+            )
+            tokens = torch.cat([tokens, tokens], dim=0)
+            tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
+
+        loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
+        loss = loss_ctc
+        if stage != sb.Stage.TRAIN:
+            # Decode token terms to words
+            predicted_words =[]
+            for logs in p_ctc:
+                text = decoder.decode(logs.detach().cpu().numpy())
+                predicted_words.append(text.split(" "))
+
+            target_words = [wrd.split(" ") for wrd in batch.wrd]
+            self.wer_metric.append(ids, predicted_words, target_words)
+            self.cer_metric.append(ids, predicted_words, target_words)
+
+        return loss
+
+    def fit_batch(self, batch):
+        """Train the parameters given a single batch in input"""
+        predictions = self.compute_forward(batch, sb.Stage.TRAIN)
+        loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
+        loss.backward()
+        if self.check_gradients(loss):
+            self.wav2vec_optimizer.step()
+            self.model_optimizer.step()
+
+        self.wav2vec_optimizer.zero_grad()
+        self.model_optimizer.zero_grad()
+
+        return loss.detach()
+
+    def evaluate_batch(self, batch, stage):
+        """Computations needed for validation/test batches"""
+        predictions = self.compute_forward(batch, stage=stage)
+        with torch.no_grad():
+            loss = self.compute_objectives(predictions, batch, stage=stage)
+        return loss.detach()
+
+    def on_stage_start(self, stage, epoch):
+        """Gets called at the beginning of each epoch"""
+        if stage != sb.Stage.TRAIN:
+            self.cer_metric = self.hparams.cer_computer()
+            self.wer_metric = self.hparams.error_rate_computer()
+
+    def on_stage_end(self, stage, stage_loss, epoch):
+        """Gets called at the end of an epoch."""
+        # Compute/store important stats
+        stage_stats = {"loss": stage_loss}
+        if stage == sb.Stage.TRAIN:
+            self.train_stats = stage_stats
+        else:
+            stage_stats["CER"] = self.cer_metric.summarize("error_rate")
+            stage_stats["WER"] = self.wer_metric.summarize("error_rate")
+
+        # Perform end-of-iteration things, like annealing, logging, etc.
+        if stage == sb.Stage.VALID:
+            old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
+                stage_stats["loss"]
+            )
+            old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
+                stage_stats["loss"]
+            )
+            sb.nnet.schedulers.update_learning_rate(
+                self.model_optimizer, new_lr_model
+            )
+            sb.nnet.schedulers.update_learning_rate(
+                self.wav2vec_optimizer, new_lr_wav2vec
+            )
+            self.hparams.train_logger.log_stats(
+                stats_meta={
+                    "epoch": epoch,
+                    "lr_model": old_lr_model,
+                    "lr_wav2vec": old_lr_wav2vec,
+                },
+                train_stats=self.train_stats,
+                valid_stats=stage_stats,
+            )
+            self.checkpointer.save_and_keep_only(
+                meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
+            )
+        elif stage == sb.Stage.TEST:
+            self.hparams.train_logger.log_stats(
+                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
+                test_stats=stage_stats,
+            )
+            with open(self.hparams.wer_file, "w") as w:
+                self.wer_metric.write_stats(w)
+
+    def init_optimizers(self):
+        "Initializes the wav2vec2 optimizer and model optimizer"
+        self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
+            self.modules.wav2vec2.parameters()
+        )
+        self.model_optimizer = self.hparams.model_opt_class(
+            self.hparams.model.parameters()
+        )
+
+        if self.checkpointer is not None:
+            self.checkpointer.add_recoverable(
+                "wav2vec_opt", self.wav2vec_optimizer
+            )
+            self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
+
+label_encoder = sb.dataio.encoder.CTCTextEncoder()
+
+train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
+        hparams
+    )
+
+
+# We dynamicaly add the tokenizer to our brain class.
+# NB: This tokenizer corresponds to the one used for the LM!!
+decoder = build_ctcdecoder(
+    labels,
+    kenlm_model_path="tunisian.arpa",  # either .arpa or .bin file
+    alpha=0.5,  # tuned on a val set
+    beta=1,  # tuned on a val set
+)
+
+asr_brain = ASR(
+    modules=hparams["modules"],
+    hparams=hparams,
+    run_opts=run_opts,
+    checkpointer=hparams["checkpointer"],
+)
+asr_brain.device= "cpu"
+asr_brain.modules.to("cpu")
+asr_brain.tokenizer = label_encoder
+
+from enum import Enum, auto
+class Stage(Enum):
+    TRAIN = auto()
+    VALID = auto()
+    TEST = auto()
+
+asr_brain.on_evaluate_start()
+asr_brain.modules.eval()
+import gradio as gr
+def treat_wav_file(file_mic, file_upload, resamplers = resamplers,asr=asr_brain, device="cpu") :
+    
+    if (file_mic is not None) and (file_upload is not None):
+       warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+       wav = file_mic
+    elif (file_mic is None) and (file_upload is None):
+       return "ERROR: You have to either use the microphone or upload an audio file"
+    elif file_mic is not None:
+       wav = file_mic
+    else:
+       wav = file_upload
+    sig, sr = torchaudio.load(wav)
+    tensor_wav = sig.to(device)
+    resampled = resamplers[str(sr)](tensor_wav)
+    sentence = asr_brain.treat_wav(resampled)
+    return sentence
+
+gr.Interface(
+    fn=treat_wav_file, 
+    inputs=[gr.inputs.Audio(source="microphone", type='filepath', optional=True),
+        gr.inputs.Audio(source="upload", type='filepath', optional=True)]
+    ,outputs="text").launch()
+
+
+

partly_frozen_splitted_wavlm/1986/env.log

@@ -397,6 +397,8 @@ youtokentome==1.0.6
 youtube-dl==2021.6.6
 zipp==3.6.0
 ==============================
-Could not get git revision==============================
+Git revision:
+998bc54
+==============================
 CUDA version:
 11.7

partly_frozen_splitted_wavlm/1986/hyperparams.yaml

@@ -1,5 +1,5 @@
-# Generated 2023-01-08 from:
-# /home/salah/kenlm_train/to_copy/wavlm_partly_frozen.yaml
+# Generated 2023-01-14 from:
+# /home/salah/tunisian_asr/wavlm_partly_frozen.yaml
 # yamllint disable
 # ################################
 # Model: wav2vec2 + DNN + CTC

partly_frozen_splitted_wavlm/1986/log.txt

@@ -13666,3 +13666,489 @@ label 456:
 2023-01-09 12:09:13,323 - pydub.converter - DEBUG - subprocess.call(['ffmpeg', '-y', '-i', '/tmp/audio7zdmx3nh.wav', '-acodec', 'pcm_s32le', '-vn', '-f', 'wav', '-'])
 2023-01-09 14:08:36,999 - pydub.converter - DEBUG - subprocess.call(['ffmpeg', '-y', '-i', '/tmp/audiowqco3s07.wav', '-acodec', 'pcm_s32le', '-vn', '-f', 'wav', '-'])
 2023-01-09 14:09:50,859 - pydub.converter - DEBUG - subprocess.call(['ffmpeg', '-y', '-i', '/tmp/audiollawbske.wav', '-acodec', 'pcm_s32le', '-vn', '-f', 'wav', '-'])
+2023-01-14 18:34:53,242 - speechbrain.core - INFO - Beginning experiment!
+2023-01-14 18:34:53,243 - speechbrain.core - INFO - Experiment folder: partly_frozen_splitted_wavlm/1986/
+2023-01-14 18:34:54,077 - speechbrain.utils.superpowers - DEBUG - abkhazia==1.0
+absl-py==0.11.0
+aiohttp==3.8.0
+aiosignal==1.2.0
+alabaster==0.7.12
+alembic==1.7.4
+altair==4.2.0
+altgraph==0.17
+antlr4-python3-runtime==4.8
+anyio==3.6.2
+appdirs==1.4.4
+argcomplete==1.12.2
+argon2-cffi==20.1.0
+asgiref==3.6.0
+astunparse==1.6.3
+async-generator==1.10
+async-timeout==4.0.0
+attrdict==2.0.1
+attrs==20.3.0
+audeer==1.16.0
+audformat==0.11.5
+audinterface==0.7.0
+audiofile==1.0.0
+audiomentations==0.25.0
+audioread==2.1.9
+audobject==0.4.14
+audresample==0.1.6
+-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
+autopage==0.4.0
+Babel==2.9.0
+backcall==0.2.0
+beautifulsoup4==4.10.0
+black==19.10b0
+bleach==3.3.0
+boto3==1.20.2
+botocore==1.23.2
+braceexpand==0.1.7
+cachetools==4.2.0
+certifi @ file:///croot/certifi_1671487769961/work/certifi
+cffi==1.14.3
+cfgv==3.2.0
+chardet==3.0.4
+charset-normalizer==2.0.7
+click==7.1.2
+cliff==3.9.0
+clldutils==3.5.4
+cmaes==0.8.2
+cmake==3.18.4.post1
+cmd2==2.2.0
+colorama==0.4.4
+colorlog==4.6.2
+configparser==5.1.0
+cryptography==38.0.4
+csvw==1.8.1
+cycler==0.10.0
+Cython==0.29.21
+dataclasses==0.6
+datasets==1.5.0
+decorator==4.4.2
+deepspeech==0.9.1
+defusedxml==0.7.1
+denoiser==0.1.5
+dill==0.3.3
+Distance==0.1.3
+distlib==0.3.1
+Django==3.2.16
+django-auditlog==2.2.1
+django-filter==22.1
+django-js-asset==1.2.2
+django-mptt==0.14.0
+djangorestframework==3.14.0
+docker-pycreds==0.4.0
+docopt==0.6.2
+docutils==0.16
+drf-excel==2.2.0
+drf-flex-fields==1.0.0
+drf-renderer-xlsx==0.4.1
+easyocr==1.2.1
+editdistance==0.6.0
+emoji==2.2.0
+entrypoints==0.3
+et-xmlfile==1.1.0
+exceptiongroup==1.1.0
+farasapy==0.0.14
+fastapi==0.89.0
+fasttext==0.9.2
+ffmpeg-python==0.2.0
+ffmpy==0.3.0
+filelock==3.0.12
+flake8==3.7.9
+flatbuffers==1.12
+frozendict==2.0.7
+frozenlist==1.2.0
+fsspec==2021.11.0
+future==0.18.2
+g2p-en==2.1.0
+gast==0.3.3
+gdown==4.2.0
+gensim==4.0.1
+gitdb==4.0.9
+GitPython==3.1.24
+google-auth==1.24.0
+google-auth-oauthlib==0.4.2
+google-pasta==0.2.0
+gradio==3.16.0
+greenlet==1.1.2
+grpcio==1.32.0
+h11==0.14.0
+h5features==1.3.2
+h5py==2.10.0
+htk-io==0.5
+httpcore==0.16.3
+httpx==0.23.3
+huggingface-hub==0.9.1
+hydra-colorlog==0.1.4
+hydra-core==0.11.3
+HyperPyYAML==1.1.0
+hypothesis==6.61.2
+identify==1.5.10
+idna==2.10
+imageio==2.9.0
+imagesize==1.2.0
+importlib-metadata==4.8.1
+importlib-resources==5.2.2
+inflect==5.3.0
+ipadic==1.0.0
+ipykernel==5.3.4
+ipython==7.19.0
+ipython-genutils==0.2.0
+ipywebrtc==0.6.0
+ipywidgets==7.6.3
+iso-639==0.4.5
+isodate==0.6.0
+isort==4.3.21
+jedi==0.17.2
+jieba==0.42.1
+Jinja2==2.11.2
+jiwer==2.2.0
+jmespath==0.10.0
+joblib==0.17.0
+jsonschema==3.2.0
+julius==0.2.7
+jupyter-client==6.1.7
+jupyter-core==4.7.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+kaitaistruct==0.9
+kaldi-io==0.9.4
+kaldi-python-io==1.2.2
+kaldiio==2.17.2
+kenlm @ https://github.com/kpu/kenlm/archive/master.zip
+Keras-Preprocessing==1.1.2
+kiwisolver==1.3.1
+lang-trans==0.6.0
+latexcodec==2.0.1
+ldap3==2.9.1
+librosa==0.9.0
+linkify-it-py==1.0.3
+llvmlite==0.35.0
+lxml==4.9.0
+Mako==1.1.5
+Markdown==3.3.3
+markdown-it-py==2.1.0
+MarkupSafe==1.1.1
+marshmallow==3.14.0
+matplotlib==3.3.3
+mccabe==0.6.1
+mcd==0.4
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mecab-python3==1.0.3
+megatron-lm==2.2.0
+mido==1.2.10
+mistune==0.8.4
+more-itertools==8.6.0
+mpmath==1.2.1
+multidict==5.2.0
+multiprocess==0.70.11.1
+nbclient==0.5.3
+nbconvert==6.0.7
+nbformat==5.1.3
+NEMO==4.3.2
+nemo-toolkit==1.4.0
+nest-asyncio==1.5.1
+networkx==2.5
+nltk==3.5
+nodeenv==1.5.0
+notebook==6.3.0
+numba==0.52.0
+numpy==1.19.4
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+oauthlib==3.1.0
+omegaconf==1.4.1
+onnx==1.10.2
+OpenCC==1.1.2
+opencv-python==4.4.0.46
+openpyxl==3.0.9
+opensmile==2.2.0
+opt-einsum==3.3.0
+optuna==2.10.0
+orjson==3.8.4
+oyaml==1.0
+packaging==22.0
+pandas==1.2.5
+pandocfilters==1.4.3
+pangu==4.0.6.1
+parameterized==0.8.1
+parso==0.7.1
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+pyasn1==0.4.8
+pyasn1-modules==0.2.8
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+pybtex==0.24.0
+pybtex-docutils==1.0.1
+pycodestyle==2.5.0
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+pyctcdecode==0.4.0
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+pyDeprecate==0.3.1
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+Pygments==2.7.2
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+pypinyin==0.43.0
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+-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
+seaborn==0.11.1
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+
+
+2023-01-14 18:34:54,121 - speechbrain.utils.superpowers - DEBUG - 998bc54
+
+
+2023-01-14 18:34:54,203 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/1986//save/label_encoder.txt
+2023-01-14 18:34:54,203 - speechbrain.dataio.encoder - INFO - Load called, but CTCTextEncoder is not empty. Loaded data will overwrite everything. This is normal if there is e.g. an unk label defined at init.
+2023-01-14 18:34:54,204 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/1986//save/label_encoder.txt
+2023-01-14 18:34:55,862 - pyctcdecode.decoder - INFO - Using arpa instead of binary LM file, decoder instantiation might be slow.
+2023-01-14 18:34:55,934 - pyctcdecode.alphabet - INFO - Alphabet determined to be of regular style.
+2023-01-14 18:34:55,934 - pyctcdecode.alphabet - WARNING - Found entries of length > 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?
+2023-01-14 18:34:56,578 - speechbrain.core - INFO - Info: auto_mix_prec arg from hparam file is used
+2023-01-14 18:34:56,578 - speechbrain.core - INFO - Info: ckpt_interval_minutes arg from hparam file is used
+2023-01-14 18:34:58,666 - speechbrain.core - INFO - 313.4M trainable parameters in ASR
+2023-01-14 18:34:59,342 - speechbrain.utils.checkpoints - INFO - Loading a checkpoint from partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00
+2023-01-14 18:35:00,732 - matplotlib - DEBUG - (private) matplotlib data path: /home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/matplotlib-3.3.3-py3.8-linux-x86_64.egg/matplotlib/mpl-data
+2023-01-14 18:35:00,732 - matplotlib - DEBUG - matplotlib data path: /home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/matplotlib-3.3.3-py3.8-linux-x86_64.egg/matplotlib/mpl-data
+2023-01-14 18:35:00,736 - matplotlib - DEBUG - CONFIGDIR=/home/salah/.config/matplotlib
+2023-01-14 18:35:00,738 - matplotlib - DEBUG - matplotlib version 3.3.3
+2023-01-14 18:35:00,738 - matplotlib - DEBUG - interactive is False
+2023-01-14 18:35:00,738 - matplotlib - DEBUG - platform is linux
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'ctypes', '_opcode', 'opcode', 'dis', 'collections.abc', 'token', 'tokenize', 'linecache', 'inspect', 'traceback', 'typing.io', 'typing.re', 'typing', 'torch._utils', 'fnmatch', 'errno', 'zlib', '_compression', '_weakrefset', 'threading', '_bz2', 'bz2', '_lzma', 'lzma', 'pwd', 'grp', 'shutil', 'math', '_bisect', 'bisect', '_sha512', '_random', 'random', 'weakref', 'tempfile', 'torch._utils_internal', 'torch.version', 'torch.torch_version', 'torch._six', 'signal', '_posixsubprocess', 'select', 'selectors', 'subprocess', 'torch._C._onnx', 'torch._C._jit', 'torch._C._jit_tree_views', 'torch._C._te', 'torch._C._nvfuser', 'atexit', 'torch._C._monitor', 'torch._C._functorch', 'torch._C._profiler', 'torch._C.cpp', 'torch._C.cpp.nn', 'torch._C._lazy', 'torch._C._lazy_ts_backend', 'torch._C._itt', 'torch._C._cudart', 'torch._C._nvtx', 'torch._C._cudnn', 'torch._C._verbose', 'torch._C', 'torch._C._fft', 'torch._C._linalg', 'torch._C._nested', 'torch._C._nn', 'torch._C._return_types', 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+2023-01-14 18:35:02,821 - PIL.Image - DEBUG - Importing IcnsImagePlugin
+2023-01-14 18:35:02,822 - PIL.Image - DEBUG - Importing IcoImagePlugin
+2023-01-14 18:35:02,823 - PIL.Image - DEBUG - Importing ImImagePlugin
+2023-01-14 18:35:02,823 - PIL.Image - DEBUG - Importing ImtImagePlugin
+2023-01-14 18:35:02,824 - PIL.Image - DEBUG - Importing IptcImagePlugin
+2023-01-14 18:35:02,824 - PIL.Image - DEBUG - Importing JpegImagePlugin
+2023-01-14 18:35:02,826 - PIL.Image - DEBUG - Importing Jpeg2KImagePlugin
+2023-01-14 18:35:02,826 - PIL.Image - DEBUG - Importing McIdasImagePlugin
+2023-01-14 18:35:02,827 - PIL.Image - DEBUG - Importing MicImagePlugin
+2023-01-14 18:35:02,827 - PIL.Image - DEBUG - Image: failed to import MicImagePlugin: No module named 'olefile'
+2023-01-14 18:35:02,827 - PIL.Image - DEBUG - Importing MpegImagePlugin
+2023-01-14 18:35:02,828 - PIL.Image - DEBUG - Importing MpoImagePlugin
+2023-01-14 18:35:02,828 - PIL.Image - DEBUG - Importing MspImagePlugin
+2023-01-14 18:35:02,829 - PIL.Image - DEBUG - Importing PalmImagePlugin
+2023-01-14 18:35:02,830 - PIL.Image - DEBUG - Importing PcdImagePlugin
+2023-01-14 18:35:02,831 - PIL.Image - DEBUG - Importing PcxImagePlugin
+2023-01-14 18:35:02,831 - PIL.Image - DEBUG - Importing PdfImagePlugin
+2023-01-14 18:35:02,844 - PIL.Image - DEBUG - Importing PixarImagePlugin
+2023-01-14 18:35:02,844 - PIL.Image - DEBUG - Importing PngImagePlugin
+2023-01-14 18:35:02,845 - PIL.Image - DEBUG - Importing PpmImagePlugin
+2023-01-14 18:35:02,845 - PIL.Image - DEBUG - Importing PsdImagePlugin
+2023-01-14 18:35:02,846 - PIL.Image - DEBUG - Importing SgiImagePlugin
+2023-01-14 18:35:02,846 - PIL.Image - DEBUG - Importing SpiderImagePlugin
+2023-01-14 18:35:02,847 - PIL.Image - DEBUG - Importing SunImagePlugin
+2023-01-14 18:35:02,847 - PIL.Image - DEBUG - Importing TgaImagePlugin
+2023-01-14 18:35:02,848 - PIL.Image - DEBUG - Importing TiffImagePlugin
+2023-01-14 18:35:02,848 - PIL.Image - DEBUG - Importing WebPImagePlugin
+2023-01-14 18:35:02,850 - PIL.Image - DEBUG - Importing WmfImagePlugin
+2023-01-14 18:35:02,851 - PIL.Image - DEBUG - Importing XbmImagePlugin
+2023-01-14 18:35:02,852 - PIL.Image - DEBUG - Importing XpmImagePlugin
+2023-01-14 18:35:02,853 - PIL.Image - DEBUG - Importing XVThumbImagePlugin
+2023-01-14 18:35:03,095 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): checkip.amazonaws.com:443
+2023-01-14 18:35:03,687 - urllib3.connectionpool - DEBUG - https://checkip.amazonaws.com:443 "GET / HTTP/1.1" 200 13
+2023-01-14 18:35:03,695 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): api.gradio.app:443
+2023-01-14 18:35:04,226 - urllib3.connectionpool - DEBUG - https://api.gradio.app:443 "POST /gradio-initiated-analytics/ HTTP/1.1" 200 None
+2023-01-14 18:35:04,232 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): api.gradio.app:443
+2023-01-14 18:35:05,023 - urllib3.connectionpool - DEBUG - https://api.gradio.app:443 "POST /gradio-initiated-analytics/ HTTP/1.1" 200 None
+2023-01-14 18:35:05,031 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): api.gradio.app:443
+2023-01-14 18:35:05,756 - urllib3.connectionpool - DEBUG - https://api.gradio.app:443 "GET /pkg-version HTTP/1.1" 200 21
+2023-01-14 18:35:05,763 - asyncio - DEBUG - Using selector: EpollSelector
+2023-01-14 18:35:05,832 - asyncio - DEBUG - Using selector: EpollSelector
+2023-01-14 18:35:05,859 - urllib3.connectionpool - DEBUG - Starting new HTTP connection (1): 127.0.0.1:7860
+2023-01-14 18:35:05,864 - urllib3.connectionpool - DEBUG - http://127.0.0.1:7860 "GET /startup-events HTTP/1.1" 200 4
+2023-01-14 18:35:05,867 - urllib3.connectionpool - DEBUG - Starting new HTTP connection (1): 127.0.0.1:7860
+2023-01-14 18:35:05,878 - urllib3.connectionpool - DEBUG - http://127.0.0.1:7860 "HEAD / HTTP/1.1" 200 0
+2023-01-14 18:35:05,880 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): api.gradio.app:443
+2023-01-14 18:35:06,576 - urllib3.connectionpool - DEBUG - https://api.gradio.app:443 "POST /gradio-launched-analytics/ HTTP/1.1" 200 None
+2023-01-14 18:35:32,164 - pydub.converter - DEBUG - subprocess.call(['ffmpeg', '-y', '-i', '/tmp/audioqsk1vedg.wav', '-acodec', 'pcm_s32le', '-vn', '-f', 'wav', '-'])