class EncDecCTCModel(ASRModel, ExportableEncDecModel): """Base class for encoder decoder CTC-based models.""" @classmethod def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] model = PretrainedModelInfo( pretrained_model_name="QuartzNet15x5Base-En", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo", description= "QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="QuartzNet15x5Base-Zh", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-Zh.nemo", description= "QuartzNet15x5 model trained on ai-shell2 Mandarin Chinese dataset.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="QuartzNet5x5LS-En", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet5x5LS-En.nemo", description= "QuartzNet5x5 model trained on LibriSpeech dataset only. The model achieves a WER of 5.37% on LibriSpeech dev-clean, and a WER of 15.69% on dev-other.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="QuartzNet15x5NR-En", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5NR-En.nemo", description= "QuartzNet15x5Base-En was finetuned with RIR and noise augmentation to make it more robust to noise. This model should be preferred for noisy speech transcription. This model achieves a WER of 3.96% on LibriSpeech dev-clean and a WER of 10.14% on dev-other.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="Jasper10x5Dr-En", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/Jasper10x5Dr-En.nemo", description= "JasperNet10x5Dr model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1. The model achieves a WER of 3.37% on LibriSpeech dev-clean, 9.81% on dev-other.", ) result.append(model) return result def __init__(self, cfg: DictConfig, trainer: Trainer = None): # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable self.global_rank = 0 self.world_size = 1 self.local_rank = 0 if trainer is not None: self.global_rank = (trainer.node_rank * trainer.num_gpus) + trainer.local_rank self.world_size = trainer.num_nodes * trainer.num_gpus self.local_rank = trainer.local_rank super().__init__(cfg=cfg, trainer=trainer) self.preprocessor = EncDecCTCModel.from_config_dict( self._cfg.preprocessor) self.encoder = EncDecCTCModel.from_config_dict(self._cfg.encoder) with open_dict(self._cfg): if "feat_in" not in self._cfg.decoder or ( not self._cfg.decoder.feat_in and hasattr(self.encoder, '_feat_out')): self._cfg.decoder.feat_in = self.encoder._feat_out if "feat_in" not in self._cfg.decoder or not self._cfg.decoder.feat_in: raise ValueError( "param feat_in of the decoder's config is not set!") self.decoder = EncDecCTCModel.from_config_dict(self._cfg.decoder) self.loss = CTCLoss( num_classes=self.decoder.num_classes_with_blank - 1, zero_infinity=True, reduction=self._cfg.get("ctc_reduction", "mean_batch"), ) if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None: self.spec_augmentation = EncDecCTCModel.from_config_dict( self._cfg.spec_augment) else: self.spec_augmentation = None # Setup metric objects self._wer = WER( vocabulary=self.decoder.vocabulary, batch_dim_index=0, use_cer=self._cfg.get('use_cer', False), ctc_decode=True, dist_sync_on_step=True, log_prediction=self._cfg.get("log_prediction", False), ) @torch.no_grad() def transcribe(self, paths2audio_files: List[str], batch_size: int = 4, logprobs=False) -> List[str]: """ Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. Args: paths2audio_files: (a list) of paths to audio files. \ Recommended length per file is between 5 and 25 seconds. \ But it is possible to pass a few hours long file if enough GPU memory is available. batch_size: (int) batch size to use during inference. \ Bigger will result in better throughput performance but would use more memory. logprobs: (bool) pass True to get log probabilities instead of transcripts. Returns: A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files """ if paths2audio_files is None or len(paths2audio_files) == 0: return {} # We will store transcriptions here hypotheses = [] # Model's mode and device mode = self.training device = next(self.parameters()).device dither_value = self.preprocessor.featurizer.dither pad_to_value = self.preprocessor.featurizer.pad_to try: self.preprocessor.featurizer.dither = 0.0 self.preprocessor.featurizer.pad_to = 0 # Switch model to evaluation mode self.eval() logging_level = logging.get_verbosity() logging.set_verbosity(logging.WARNING) # Work in tmp directory - will store manifest file there with tempfile.TemporaryDirectory() as tmpdir: with open(os.path.join(tmpdir, 'manifest.json'), 'w') as fp: for audio_file in paths2audio_files: entry = { 'audio_filepath': audio_file, 'duration': 100000, 'text': 'nothing' } fp.write(json.dumps(entry) + '\n') config = { 'paths2audio_files': paths2audio_files, 'batch_size': batch_size, 'temp_dir': tmpdir } temporary_datalayer = self._setup_transcribe_dataloader(config) for test_batch in temporary_datalayer: logits, logits_len, greedy_predictions = self.forward( input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device)) if logprobs: # dump log probs per file for idx in range(logits.shape[0]): hypotheses.append(logits[idx][:logits_len[idx]]) else: hypotheses += self._wer.ctc_decoder_predictions_tensor( greedy_predictions, predictions_len=logits_len) del test_batch finally: # set mode back to its original value self.train(mode=mode) self.preprocessor.featurizer.dither = dither_value self.preprocessor.featurizer.pad_to = pad_to_value logging.set_verbosity(logging_level) return hypotheses def change_vocabulary(self, new_vocabulary: List[str]): """ Changes vocabulary used during CTC decoding process. Use this method when fine-tuning on from pre-trained model. This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need model to learn capitalization, punctuation and/or special characters. If new_vocabulary == self.decoder.vocabulary then nothing will be changed. Args: new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ this is target alphabet. Returns: None """ if self.decoder.vocabulary == new_vocabulary: logging.warning( f"Old {self.decoder.vocabulary} and new {new_vocabulary} match. Not changing anything." ) else: if new_vocabulary is None or len(new_vocabulary) == 0: raise ValueError( f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}' ) decoder_config = self.decoder.to_config_dict() new_decoder_config = copy.deepcopy(decoder_config) new_decoder_config['vocabulary'] = new_vocabulary new_decoder_config['num_classes'] = len(new_vocabulary) del self.decoder self.decoder = EncDecCTCModel.from_config_dict(new_decoder_config) del self.loss self.loss = CTCLoss( num_classes=self.decoder.num_classes_with_blank - 1, zero_infinity=True, reduction=self._cfg.get("ctc_reduction", "mean_batch"), ) self._wer = WER( vocabulary=self.decoder.vocabulary, batch_dim_index=0, use_cer=self._cfg.get('use_cer', False), ctc_decode=True, dist_sync_on_step=True, log_prediction=self._cfg.get("log_prediction", False), ) # Update config OmegaConf.set_struct(self._cfg.decoder, False) self._cfg.decoder = new_decoder_config OmegaConf.set_struct(self._cfg.decoder, True) logging.info( f"Changed decoder to output to {self.decoder.vocabulary} vocabulary." ) def _setup_dataloader_from_config(self, config: Optional[Dict]): if 'augmentor' in config: augmentor = process_augmentations(config['augmentor']) else: augmentor = None shuffle = config['shuffle'] device = 'gpu' if torch.cuda.is_available() else 'cpu' if config.get('use_dali', False): device_id = self.local_rank if device == 'gpu' else None dataset = audio_to_text_dataset.get_dali_char_dataset( config=config, shuffle=shuffle, device_id=device_id, global_rank=self.global_rank, world_size=self.world_size, preprocessor_cfg=self._cfg.preprocessor, ) return dataset # Instantiate tarred dataset loader or normal dataset loader if config.get('is_tarred', False): if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( 'manifest_filepath' in config and config['manifest_filepath'] is None): logging.warning( "Could not load dataset as `manifest_filepath` was None or " f"`tarred_audio_filepaths` is None. Provided config : {config}" ) return None shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 dataset = audio_to_text_dataset.get_tarred_char_dataset( config=config, shuffle_n=shuffle_n, global_rank=self.global_rank, world_size=self.world_size, augmentor=augmentor, ) shuffle = False else: if 'manifest_filepath' in config and config[ 'manifest_filepath'] is None: logging.warning( f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}" ) return None dataset = audio_to_text_dataset.get_char_dataset( config=config, augmentor=augmentor) return torch.utils.data.DataLoader( dataset=dataset, batch_size=config['batch_size'], collate_fn=dataset.collate_fn, drop_last=config.get('drop_last', False), shuffle=shuffle, num_workers=config.get('num_workers', 0), pin_memory=config.get('pin_memory', False), ) def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): if 'shuffle' not in train_data_config: train_data_config['shuffle'] = True # preserve config self._update_dataset_config(dataset_name='train', config=train_data_config) self._train_dl = self._setup_dataloader_from_config( config=train_data_config) # Need to set this because if using an IterableDataset, the length of the dataloader is the total number # of samples rather than the number of batches, and this messes up the tqdm progress bar. # So we set the number of steps manually (to the correct number) to fix this. if 'is_tarred' in train_data_config and train_data_config['is_tarred']: # We also need to check if limit_train_batches is already set. # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). if isinstance(self._trainer.limit_train_batches, float): self._trainer.limit_train_batches = int( self._trainer.limit_train_batches * ceil( (len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size'])) def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): if 'shuffle' not in val_data_config: val_data_config['shuffle'] = False # preserve config self._update_dataset_config(dataset_name='validation', config=val_data_config) self._validation_dl = self._setup_dataloader_from_config( config=val_data_config) def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): if 'shuffle' not in test_data_config: test_data_config['shuffle'] = False # preserve config self._update_dataset_config(dataset_name='test', config=test_data_config) self._test_dl = self._setup_dataloader_from_config( config=test_data_config) @property def input_types(self) -> Optional[Dict[str, NeuralType]]: if hasattr(self.preprocessor, '_sample_rate'): input_signal_eltype = AudioSignal( freq=self.preprocessor._sample_rate) else: input_signal_eltype = AudioSignal() return { "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), } @property def output_types(self) -> Optional[Dict[str, NeuralType]]: return { "outputs": NeuralType(('B', 'T', 'D'), LogprobsType()), "encoded_lengths": NeuralType(tuple('B'), LengthsType()), "greedy_predictions": NeuralType(('B', 'T'), LabelsType()), } @typecheck() def forward(self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None): has_input_signal = input_signal is not None and input_signal_length is not None has_processed_signal = processed_signal is not None and processed_signal_length is not None if (has_input_signal ^ has_processed_signal) == False: raise ValueError( f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " " with ``processed_signal`` and ``processed_signal_len`` arguments." ) if not has_processed_signal: processed_signal, processed_signal_length = self.preprocessor( input_signal=input_signal, length=input_signal_length, ) if self.spec_augmentation is not None and self.training: processed_signal = self.spec_augmentation( input_spec=processed_signal) encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) log_probs = self.decoder(encoder_output=encoded) greedy_predictions = log_probs.argmax(dim=-1, keepdim=False) return log_probs, encoded_len, greedy_predictions # PTL-specific methods def training_step(self, batch, batch_nb): signal, signal_len, transcript, transcript_len = batch if isinstance(batch, DALIOutputs) and batch.has_processed_signal: log_probs, encoded_len, predictions = self.forward( processed_signal=signal, processed_signal_length=signal_len) else: log_probs, encoded_len, predictions = self.forward( input_signal=signal, input_signal_length=signal_len) loss_value = self.loss(log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len) tensorboard_logs = { 'train_loss': loss_value, 'learning_rate': self._optimizer.param_groups[0]['lr'] } if hasattr(self, '_trainer') and self._trainer is not None: log_every_n_steps = self._trainer.log_every_n_steps else: log_every_n_steps = 1 if (batch_nb + 1) % log_every_n_steps == 0: self._wer.update( predictions=predictions, targets=transcript, target_lengths=transcript_len, predictions_lengths=encoded_len, ) wer, _, _ = self._wer.compute() tensorboard_logs.update({'training_batch_wer': wer}) return {'loss': loss_value, 'log': tensorboard_logs} def validation_step(self, batch, batch_idx, dataloader_idx=0): signal, signal_len, transcript, transcript_len = batch if isinstance(batch, DALIOutputs) and batch.has_processed_signal: log_probs, encoded_len, predictions = self.forward( processed_signal=signal, processed_signal_length=signal_len) else: log_probs, encoded_len, predictions = self.forward( input_signal=signal, input_signal_length=signal_len) loss_value = self.loss(log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len) self._wer.update(predictions=predictions, targets=transcript, target_lengths=transcript_len, predictions_lengths=encoded_len) wer, wer_num, wer_denom = self._wer.compute() return { 'val_loss': loss_value, 'val_wer_num': wer_num, 'val_wer_denom': wer_denom, 'val_wer': wer, } def test_step(self, batch, batch_idx, dataloader_idx=0): logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) test_logs = { 'test_loss': logs['val_loss'], 'test_wer_num': logs['val_wer_num'], 'test_wer_denom': logs['val_wer_denom'], 'test_wer': logs['val_wer'], } return test_logs def test_dataloader(self): if self._test_dl is not None: return self._test_dl def _setup_transcribe_dataloader( self, config: Dict) -> 'torch.utils.data.DataLoader': """ Setup function for a temporary data loader which wraps the provided audio file. Args: config: A python dictionary which contains the following keys: paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ Recommended length per file is between 5 and 25 seconds. batch_size: (int) batch size to use during inference. \ Bigger will result in better throughput performance but would use more memory. temp_dir: (str) A temporary directory where the audio manifest is temporarily stored. Returns: A pytorch DataLoader for the given audio file(s). """ dl_config = { 'manifest_filepath': os.path.join(config['temp_dir'], 'manifest.json'), 'sample_rate': self.preprocessor._sample_rate, 'labels': self.decoder.vocabulary, 'batch_size': min(config['batch_size'], len(config['paths2audio_files'])), 'trim_silence': True, 'shuffle': False, } temporary_datalayer = self._setup_dataloader_from_config( config=DictConfig(dl_config)) return temporary_datalayer
class EncDecCTCModel(ASRModel, ExportableEncDecModel, ASRModuleMixin): """Base class for encoder decoder CTC-based models.""" @classmethod def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ results = [] model = PretrainedModelInfo( pretrained_model_name="QuartzNet15x5Base-En", description="QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. Please visit https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels for further details.", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_quartznet15x5/versions/1.0.0rc1/files/stt_en_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_jasper10x5dr", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_jasper10x5dr", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_jasper10x5dr/versions/1.0.0rc1/files/stt_en_jasper10x5dr.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_ca_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_quartznet15x5/versions/1.0.0rc1/files/stt_ca_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_it_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_quartznet15x5/versions/1.0.0rc1/files/stt_it_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_fr_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_quartznet15x5/versions/1.0.0rc1/files/stt_fr_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_es_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_quartznet15x5/versions/1.0.0rc1/files/stt_es_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_de_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_quartznet15x5/versions/1.0.0rc1/files/stt_de_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_pl_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_pl_quartznet15x5/versions/1.0.0rc1/files/stt_pl_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_ru_quartznet15x5", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_quartznet15x5", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_quartznet15x5/versions/1.0.0rc1/files/stt_ru_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_zh_citrinet_512", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_512", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_512/versions/1.0.0rc1/files/stt_zh_citrinet_512.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_zh_citrinet_1024_gamma_0_25", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_zh_citrinet_1024_gamma_0_25", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="asr_talknet_aligner", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:asr_talknet_aligner", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/asr_talknet_aligner/versions/1.0.0rc1/files/qn5x5_libri_tts_phonemes.nemo", ) results.append(model) return results def __init__(self, cfg: DictConfig, trainer: Trainer = None): # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 self.world_size = 1 if trainer is not None: self.world_size = trainer.world_size super().__init__(cfg=cfg, trainer=trainer) self.preprocessor = EncDecCTCModel.from_config_dict(self._cfg.preprocessor) self.encoder = EncDecCTCModel.from_config_dict(self._cfg.encoder) with open_dict(self._cfg): if "feat_in" not in self._cfg.decoder or ( not self._cfg.decoder.feat_in and hasattr(self.encoder, '_feat_out') ): self._cfg.decoder.feat_in = self.encoder._feat_out if "feat_in" not in self._cfg.decoder or not self._cfg.decoder.feat_in: raise ValueError("param feat_in of the decoder's config is not set!") if self.cfg.decoder.num_classes < 1 and self.cfg.decoder.vocabulary is not None: logging.info( "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format( self.cfg.decoder.num_classes, len(self.cfg.decoder.vocabulary) ) ) cfg.decoder["num_classes"] = len(self.cfg.decoder.vocabulary) self.decoder = EncDecCTCModel.from_config_dict(self._cfg.decoder) self.loss = CTCLoss( num_classes=self.decoder.num_classes_with_blank - 1, zero_infinity=True, reduction=self._cfg.get("ctc_reduction", "mean_batch"), ) if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None: self.spec_augmentation = EncDecCTCModel.from_config_dict(self._cfg.spec_augment) else: self.spec_augmentation = None # Setup metric objects self._wer = WER( vocabulary=self.decoder.vocabulary, batch_dim_index=0, use_cer=self._cfg.get('use_cer', False), ctc_decode=True, dist_sync_on_step=True, log_prediction=self._cfg.get("log_prediction", False), ) @torch.no_grad() def transcribe( self, paths2audio_files: List[str], batch_size: int = 4, logprobs: bool = False, return_hypotheses: bool = False, num_workers: int = 0, ) -> List[str]: """ Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. Args: paths2audio_files: (a list) of paths to audio files. \ Recommended length per file is between 5 and 25 seconds. \ But it is possible to pass a few hours long file if enough GPU memory is available. batch_size: (int) batch size to use during inference. Bigger will result in better throughput performance but would use more memory. logprobs: (bool) pass True to get log probabilities instead of transcripts. return_hypotheses: (bool) Either return hypotheses or text With hypotheses can do some postprocessing like getting timestamp or rescoring num_workers: (int) number of workers for DataLoader Returns: A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files """ if paths2audio_files is None or len(paths2audio_files) == 0: return {} if return_hypotheses and logprobs: raise ValueError( "Either `return_hypotheses` or `logprobs` can be True at any given time." "Returned hypotheses will contain the logprobs." ) if num_workers is None: num_workers = min(batch_size, os.cpu_count() - 1) # We will store transcriptions here hypotheses = [] # Model's mode and device mode = self.training device = next(self.parameters()).device dither_value = self.preprocessor.featurizer.dither pad_to_value = self.preprocessor.featurizer.pad_to try: self.preprocessor.featurizer.dither = 0.0 self.preprocessor.featurizer.pad_to = 0 # Switch model to evaluation mode self.eval() # Freeze the encoder and decoder modules self.encoder.freeze() self.decoder.freeze() logging_level = logging.get_verbosity() logging.set_verbosity(logging.WARNING) # Work in tmp directory - will store manifest file there with tempfile.TemporaryDirectory() as tmpdir: with open(os.path.join(tmpdir, 'manifest.json'), 'w', encoding='utf-8') as fp: for audio_file in paths2audio_files: entry = {'audio_filepath': audio_file, 'duration': 100000, 'text': ''} fp.write(json.dumps(entry) + '\n') config = { 'paths2audio_files': paths2audio_files, 'batch_size': batch_size, 'temp_dir': tmpdir, 'num_workers': num_workers, } temporary_datalayer = self._setup_transcribe_dataloader(config) for test_batch in tqdm(temporary_datalayer, desc="Transcribing"): logits, logits_len, greedy_predictions = self.forward( input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device) ) if logprobs: # dump log probs per file for idx in range(logits.shape[0]): lg = logits[idx][: logits_len[idx]] hypotheses.append(lg.cpu().numpy()) else: current_hypotheses = self._wer.ctc_decoder_predictions_tensor( greedy_predictions, predictions_len=logits_len, return_hypotheses=return_hypotheses, ) if return_hypotheses: # dump log probs per file for idx in range(logits.shape[0]): current_hypotheses[idx].y_sequence = logits[idx][: logits_len[idx]] hypotheses += current_hypotheses del greedy_predictions del logits del test_batch finally: # set mode back to its original value self.train(mode=mode) self.preprocessor.featurizer.dither = dither_value self.preprocessor.featurizer.pad_to = pad_to_value if mode is True: self.encoder.unfreeze() self.decoder.unfreeze() logging.set_verbosity(logging_level) return hypotheses def change_vocabulary(self, new_vocabulary: List[str]): """ Changes vocabulary used during CTC decoding process. Use this method when fine-tuning on from pre-trained model. This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need model to learn capitalization, punctuation and/or special characters. If new_vocabulary == self.decoder.vocabulary then nothing will be changed. Args: new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ this is target alphabet. Returns: None """ if self.decoder.vocabulary == new_vocabulary: logging.warning(f"Old {self.decoder.vocabulary} and new {new_vocabulary} match. Not changing anything.") else: if new_vocabulary is None or len(new_vocabulary) == 0: raise ValueError(f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}') decoder_config = self.decoder.to_config_dict() new_decoder_config = copy.deepcopy(decoder_config) new_decoder_config['vocabulary'] = new_vocabulary new_decoder_config['num_classes'] = len(new_vocabulary) del self.decoder self.decoder = EncDecCTCModel.from_config_dict(new_decoder_config) del self.loss self.loss = CTCLoss( num_classes=self.decoder.num_classes_with_blank - 1, zero_infinity=True, reduction=self._cfg.get("ctc_reduction", "mean_batch"), ) self._wer = WER( vocabulary=self.decoder.vocabulary, batch_dim_index=0, use_cer=self._cfg.get('use_cer', False), ctc_decode=True, dist_sync_on_step=True, log_prediction=self._cfg.get("log_prediction", False), ) # Update config OmegaConf.set_struct(self._cfg.decoder, False) self._cfg.decoder = new_decoder_config OmegaConf.set_struct(self._cfg.decoder, True) ds_keys = ['train_ds', 'validation_ds', 'test_ds'] for key in ds_keys: if key in self.cfg: with open_dict(self.cfg[key]): self.cfg[key]['labels'] = OmegaConf.create(new_vocabulary) logging.info(f"Changed decoder to output to {self.decoder.vocabulary} vocabulary.") def _setup_dataloader_from_config(self, config: Optional[Dict]): if 'augmentor' in config: augmentor = process_augmentations(config['augmentor']) else: augmentor = None # Automatically inject args from model config to dataloader config audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate') audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='labels') shuffle = config['shuffle'] device = 'gpu' if torch.cuda.is_available() else 'cpu' if config.get('use_dali', False): device_id = self.local_rank if device == 'gpu' else None dataset = audio_to_text_dataset.get_dali_char_dataset( config=config, shuffle=shuffle, device_id=device_id, global_rank=self.global_rank, world_size=self.world_size, preprocessor_cfg=self._cfg.preprocessor, ) return dataset # Instantiate tarred dataset loader or normal dataset loader if config.get('is_tarred', False): if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( 'manifest_filepath' in config and config['manifest_filepath'] is None ): logging.warning( "Could not load dataset as `manifest_filepath` was None or " f"`tarred_audio_filepaths` is None. Provided config : {config}" ) return None shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 dataset = audio_to_text_dataset.get_tarred_dataset( config=config, shuffle_n=shuffle_n, global_rank=self.global_rank, world_size=self.world_size, augmentor=augmentor, ) shuffle = False else: if 'manifest_filepath' in config and config['manifest_filepath'] is None: logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") return None dataset = audio_to_text_dataset.get_char_dataset(config=config, augmentor=augmentor) if hasattr(dataset, 'collate_fn'): collate_fn = dataset.collate_fn else: collate_fn = dataset.datasets[0].collate_fn return torch.utils.data.DataLoader( dataset=dataset, batch_size=config['batch_size'], collate_fn=collate_fn, drop_last=config.get('drop_last', False), shuffle=shuffle, num_workers=config.get('num_workers', 0), pin_memory=config.get('pin_memory', False), ) def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): """ Sets up the training data loader via a Dict-like object. Args: train_data_config: A config that contains the information regarding construction of an ASR Training dataset. Supported Datasets: - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` """ if 'shuffle' not in train_data_config: train_data_config['shuffle'] = True # preserve config self._update_dataset_config(dataset_name='train', config=train_data_config) self._train_dl = self._setup_dataloader_from_config(config=train_data_config) # Need to set this because if using an IterableDataset, the length of the dataloader is the total number # of samples rather than the number of batches, and this messes up the tqdm progress bar. # So we set the number of steps manually (to the correct number) to fix this. if 'is_tarred' in train_data_config and train_data_config['is_tarred']: # We also need to check if limit_train_batches is already set. # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): self._trainer.limit_train_batches = int( self._trainer.limit_train_batches * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) ) elif self._trainer is None: logging.warning( "Model Trainer was not set before constructing the dataset, incorrect number of " "training batches will be used. Please set the trainer and rebuild the dataset." ) def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): """ Sets up the validation data loader via a Dict-like object. Args: val_data_config: A config that contains the information regarding construction of an ASR Training dataset. Supported Datasets: - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` """ if 'shuffle' not in val_data_config: val_data_config['shuffle'] = False # preserve config self._update_dataset_config(dataset_name='validation', config=val_data_config) self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): """ Sets up the test data loader via a Dict-like object. Args: test_data_config: A config that contains the information regarding construction of an ASR Training dataset. Supported Datasets: - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` """ if 'shuffle' not in test_data_config: test_data_config['shuffle'] = False # preserve config self._update_dataset_config(dataset_name='test', config=test_data_config) self._test_dl = self._setup_dataloader_from_config(config=test_data_config) @property def input_types(self) -> Optional[Dict[str, NeuralType]]: if hasattr(self.preprocessor, '_sample_rate'): input_signal_eltype = AudioSignal(freq=self.preprocessor._sample_rate) else: input_signal_eltype = AudioSignal() return { "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), "sample_id": NeuralType(tuple('B'), LengthsType(), optional=True), } @property def output_types(self) -> Optional[Dict[str, NeuralType]]: return { "outputs": NeuralType(('B', 'T', 'D'), LogprobsType()), "encoded_lengths": NeuralType(tuple('B'), LengthsType()), "greedy_predictions": NeuralType(('B', 'T'), LabelsType()), } @typecheck() def forward( self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None ): """ Forward pass of the model. Args: input_signal: Tensor that represents a batch of raw audio signals, of shape [B, T]. T here represents timesteps, with 1 second of audio represented as `self.sample_rate` number of floating point values. input_signal_length: Vector of length B, that contains the individual lengths of the audio sequences. processed_signal: Tensor that represents a batch of processed audio signals, of shape (B, D, T) that has undergone processing via some DALI preprocessor. processed_signal_length: Vector of length B, that contains the individual lengths of the processed audio sequences. Returns: A tuple of 3 elements - 1) The log probabilities tensor of shape [B, T, D]. 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. 3) The greedy token predictions of the model of shape [B, T] (via argmax) """ has_input_signal = input_signal is not None and input_signal_length is not None has_processed_signal = processed_signal is not None and processed_signal_length is not None if (has_input_signal ^ has_processed_signal) == False: raise ValueError( f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " " with ``processed_signal`` and ``processed_signal_len`` arguments." ) if not has_processed_signal: processed_signal, processed_signal_length = self.preprocessor( input_signal=input_signal, length=input_signal_length, ) if self.spec_augmentation is not None and self.training: processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) log_probs = self.decoder(encoder_output=encoded) greedy_predictions = log_probs.argmax(dim=-1, keepdim=False) return log_probs, encoded_len, greedy_predictions # PTL-specific methods def training_step(self, batch, batch_nb): signal, signal_len, transcript, transcript_len = batch if isinstance(batch, DALIOutputs) and batch.has_processed_signal: log_probs, encoded_len, predictions = self.forward( processed_signal=signal, processed_signal_length=signal_len ) else: log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len) loss_value = self.loss( log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len ) tensorboard_logs = {'train_loss': loss_value, 'learning_rate': self._optimizer.param_groups[0]['lr']} if hasattr(self, '_trainer') and self._trainer is not None: log_every_n_steps = self._trainer.log_every_n_steps else: log_every_n_steps = 1 if (batch_nb + 1) % log_every_n_steps == 0: self._wer.update( predictions=predictions, targets=transcript, target_lengths=transcript_len, predictions_lengths=encoded_len, ) wer, _, _ = self._wer.compute() self._wer.reset() tensorboard_logs.update({'training_batch_wer': wer}) return {'loss': loss_value, 'log': tensorboard_logs} def predict_step(self, batch, batch_idx, dataloader_idx=0): signal, signal_len, transcript, transcript_len, sample_id = batch if isinstance(batch, DALIOutputs) and batch.has_processed_signal: log_probs, encoded_len, predictions = self.forward( processed_signal=signal, processed_signal_length=signal_len ) else: log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len) transcribed_texts = self._wer.ctc_decoder_predictions_tensor( predictions=predictions, predictions_len=encoded_len, return_hypotheses=False, ) sample_id = sample_id.cpu().detach().numpy() return list(zip(sample_id, transcribed_texts)) def validation_step(self, batch, batch_idx, dataloader_idx=0): signal, signal_len, transcript, transcript_len = batch if isinstance(batch, DALIOutputs) and batch.has_processed_signal: log_probs, encoded_len, predictions = self.forward( processed_signal=signal, processed_signal_length=signal_len ) else: log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len) loss_value = self.loss( log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len ) self._wer.update( predictions=predictions, targets=transcript, target_lengths=transcript_len, predictions_lengths=encoded_len ) wer, wer_num, wer_denom = self._wer.compute() self._wer.reset() return { 'val_loss': loss_value, 'val_wer_num': wer_num, 'val_wer_denom': wer_denom, 'val_wer': wer, } def test_step(self, batch, batch_idx, dataloader_idx=0): logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) test_logs = { 'test_loss': logs['val_loss'], 'test_wer_num': logs['val_wer_num'], 'test_wer_denom': logs['val_wer_denom'], 'test_wer': logs['val_wer'], } return test_logs def test_dataloader(self): if self._test_dl is not None: return self._test_dl def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': """ Setup function for a temporary data loader which wraps the provided audio file. Args: config: A python dictionary which contains the following keys: paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ Recommended length per file is between 5 and 25 seconds. batch_size: (int) batch size to use during inference. \ Bigger will result in better throughput performance but would use more memory. temp_dir: (str) A temporary directory where the audio manifest is temporarily stored. num_workers: (int) number of workers. Depends of the batch_size and machine. \ 0 - only the main process will load batches, 1 - one worker (not main process) Returns: A pytorch DataLoader for the given audio file(s). """ if 'manifest_filepath' in config: manifest_filepath = config['manifest_filepath'] batch_size = config['batch_size'] else: manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') batch_size = min(config['batch_size'], len(config['paths2audio_files'])) dl_config = { 'manifest_filepath': manifest_filepath, 'sample_rate': self.preprocessor._sample_rate, 'labels': self.decoder.vocabulary, 'batch_size': batch_size, 'trim_silence': False, 'shuffle': False, 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), 'pin_memory': True, } temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) return temporary_datalayer