# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import json import os import tempfile from math import ceil from typing import Dict, List, Optional, Tuple, Union import torch from lightning.pytorch import Trainer from omegaconf import DictConfig, OmegaConf, open_dict from tqdm.auto import tqdm from nemo.collections.asr.data import audio_to_text_dataset from nemo.collections.asr.losses.rnnt import RNNTLoss, resolve_rnnt_default_loss_name from nemo.collections.asr.metrics.wer import WER from nemo.collections.asr.models.asr_model import ASRModel from nemo.collections.asr.modules.rnnt import RNNTDecoderJoint from nemo.collections.asr.parts.mixins import ASRModuleMixin from nemo.collections.asr.parts.preprocessing.segment import ChannelSelectorType from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecoding, RNNTDecodingConfig from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset from nemo.core.classes import Exportable from nemo.core.classes.common import PretrainedModelInfo, typecheck from nemo.core.classes.mixins import AccessMixin from nemo.core.neural_types import AcousticEncodedRepresentation, LengthsType, NeuralType, VideoSignal from nemo.utils import logging class VisualEncDecRNNTModel(ASRModel, ASRModuleMixin, Exportable): """Base class for encoder decoder RNNT-based models.""" 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) # Preprocessors self.video_preprocessor = VisualEncDecRNNTModel.from_config_dict(self._cfg.video_preprocessor) # Augmentations self.video_augmentation = VisualEncDecRNNTModel.from_config_dict(self._cfg.video_augment) # Front-end Networks self.video_front_end = VisualEncDecRNNTModel.from_config_dict(self._cfg.video_front_end) # Back-end Networks self.encoder = VisualEncDecRNNTModel.from_config_dict(self._cfg.encoder) # Update config values required by components dynamically with open_dict(self.cfg.decoder): self.cfg.decoder.vocab_size = len(self.cfg.labels) with open_dict(self.cfg.joint): self.cfg.joint.num_classes = len(self.cfg.labels) self.cfg.joint.vocabulary = self.cfg.labels self.cfg.joint.jointnet.encoder_hidden = self.cfg.model_defaults.enc_hidden self.cfg.joint.jointnet.pred_hidden = self.cfg.model_defaults.pred_hidden self.decoder = VisualEncDecRNNTModel.from_config_dict(self.cfg.decoder) self.joint = VisualEncDecRNNTModel.from_config_dict(self.cfg.joint) # Setup RNNT Loss loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(self.cfg.get("loss", None)) self.loss = RNNTLoss( num_classes=self.joint.num_classes_with_blank - 1, loss_name=loss_name, loss_kwargs=loss_kwargs, reduction=self.cfg.get("rnnt_reduction", "mean_batch"), ) # Setup decoding objects self.decoding = RNNTDecoding( decoding_cfg=self.cfg.decoding, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, ) # Setup WER calculation self.wer = WER( decoding=self.decoding, batch_dim_index=0, use_cer=self._cfg.get('use_cer', False), log_prediction=self._cfg.get('log_prediction', True), dist_sync_on_step=True, ) # Whether to compute loss during evaluation if 'compute_eval_loss' in self.cfg: self.compute_eval_loss = self.cfg.compute_eval_loss else: self.compute_eval_loss = True # Setup fused Joint step if flag is set if self.joint.fuse_loss_wer or ( self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 ): self.joint.set_loss(self.loss) self.joint.set_wer(self.wer) # Setup optimization normalization (if provided in config) self.setup_optim_normalization() # Setup optional Optimization flags self.setup_optimization_flags() # Setup encoder adapters (from ASRAdapterModelMixin) self.setup_adapters() def setup_optim_normalization(self): """ Helper method to setup normalization of certain parts of the model prior to the optimization step. Supported pre-optimization normalizations are as follows: .. code-block:: yaml # Variation Noise injection model: variational_noise: std: 0.0 start_step: 0 # Joint - Length normalization model: normalize_joint_txu: false # Encoder Network - gradient normalization model: normalize_encoder_norm: false # Decoder / Prediction Network - gradient normalization model: normalize_decoder_norm: false # Joint - gradient normalization model: normalize_joint_norm: false """ # setting up the variational noise for the decoder if hasattr(self.cfg, 'variational_noise'): self._optim_variational_noise_std = self.cfg['variational_noise'].get('std', 0) self._optim_variational_noise_start = self.cfg['variational_noise'].get('start_step', 0) else: self._optim_variational_noise_std = 0 self._optim_variational_noise_start = 0 # Setup normalized gradients for model joint by T x U scaling factor (joint length normalization) self._optim_normalize_joint_txu = self.cfg.get('normalize_joint_txu', False) self._optim_normalize_txu = None # Setup normalized encoder norm for model self._optim_normalize_encoder_norm = self.cfg.get('normalize_encoder_norm', False) # Setup normalized decoder norm for model self._optim_normalize_decoder_norm = self.cfg.get('normalize_decoder_norm', False) # Setup normalized joint norm for model self._optim_normalize_joint_norm = self.cfg.get('normalize_joint_norm', False) def extract_rnnt_loss_cfg(self, cfg: Optional[DictConfig]): """ Helper method to extract the rnnt loss name, and potentially its kwargs to be passed. Args: cfg: Should contain `loss_name` as a string which is resolved to a RNNT loss name. If the default should be used, then `default` can be used. Optionally, one can pass additional kwargs to the loss function. The subdict should have a keyname as follows : `{loss_name}_kwargs`. Note that whichever loss_name is selected, that corresponding kwargs will be selected. For the "default" case, the "{resolved_default}_kwargs" will be used. Examples: .. code-block:: yaml loss_name: "default" warprnnt_numba_kwargs: kwargs2: some_other_val Returns: A tuple, the resolved loss name as well as its kwargs (if found). """ if cfg is None: cfg = DictConfig({}) loss_name = cfg.get("loss_name", "default") if loss_name == "default": loss_name = resolve_rnnt_default_loss_name() loss_kwargs = cfg.get(f"{loss_name}_kwargs", None) logging.info(f"Using RNNT Loss : {loss_name}\n" f"Loss {loss_name}_kwargs: {loss_kwargs}") return loss_name, loss_kwargs @torch.no_grad() def transcribe( self, paths2video_files: List[str], batch_size: int = 4, return_hypotheses: bool = False, partial_hypothesis: Optional[List['Hypothesis']] = None, num_workers: int = 0, channel_selector: Optional[ChannelSelectorType] = None, augmentor: DictConfig = None, ) -> Tuple[List[str], Optional[List['Hypothesis']]]: """ Uses greedy decoding to transcribe video files. Use this method for debugging and prototyping. Args: paths2video_files: (a list) of paths to video files. batch_size: (int) batch size to use during inference. \ Bigger will result in better throughput performance but would use more memory. 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 channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. Returns: Returns a list of greedy transcript Hypothesis or list of all Hypothesis """ if paths2video_files is None or len(paths2video_files) == 0: return {} # We will store transcriptions here hypotheses = [] # Model's mode and device mode = self.training device = next(self.parameters()).device if num_workers is None: num_workers = min(batch_size, os.cpu_count() - 1) try: # Switch model to evaluation mode self.eval() # Freeze the visual front-end, encoder and decoder modules self.video_front_end.freeze() self.encoder.freeze() self.decoder.freeze() self.joint.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 video_file in paths2video_files: entry = {'video_filepath': video_file, 'duration': 100000, 'text': ''} fp.write(json.dumps(entry) + '\n') config = { 'paths2video_files': paths2video_files, 'batch_size': batch_size, 'temp_dir': tmpdir, 'num_workers': num_workers, 'channel_selector': channel_selector, } if augmentor: config['augmentor'] = augmentor temporary_datalayer = self._setup_transcribe_dataloader(config) for test_batch in tqdm(temporary_datalayer, desc="Transcribing"): encoded, encoded_len = self.forward( input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device) ) best_hyp = self.decoding.rnnt_decoder_predictions_tensor( encoded, encoded_len, return_hypotheses=return_hypotheses, partial_hypotheses=partial_hypothesis, ) hypotheses += best_hyp del encoded del test_batch finally: # set mode back to its original value self.train(mode=mode) logging.set_verbosity(logging_level) if mode is True: self.video_front_end.unfreeze() self.encoder.unfreeze() self.decoder.unfreeze() self.joint.unfreeze() return hypotheses def change_vocabulary(self, new_vocabulary: List[str], decoding_cfg: Optional[DictConfig] = None): """ Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning a 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 data in another language, or when you'd need model to learn capitalization, punctuation and/or special characters. Args: new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ this is target alphabet. decoding_cfg: A config for the decoder, which is optional. If the decoding type needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. Returns: None """ if self.joint.vocabulary == new_vocabulary: logging.warning(f"Old {self.joint.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}') joint_config = self.joint.to_config_dict() new_joint_config = copy.deepcopy(joint_config) new_joint_config['vocabulary'] = new_vocabulary new_joint_config['num_classes'] = len(new_vocabulary) del self.joint self.joint = VisualEncDecRNNTModel.from_config_dict(new_joint_config) decoder_config = self.decoder.to_config_dict() new_decoder_config = copy.deepcopy(decoder_config) new_decoder_config.vocab_size = len(new_vocabulary) del self.decoder self.decoder = VisualEncDecRNNTModel.from_config_dict(new_decoder_config) del self.loss loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(self.cfg.get('loss', None)) self.loss = RNNTLoss( num_classes=self.joint.num_classes_with_blank - 1, loss_name=loss_name, loss_kwargs=loss_kwargs ) if decoding_cfg is None: # Assume same decoding config as before decoding_cfg = self.cfg.decoding # Assert the decoding config with all hyper parameters decoding_cls = OmegaConf.structured(RNNTDecodingConfig) decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) self.decoding = RNNTDecoding( decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, ) self.wer = WER( decoding=self.decoding, batch_dim_index=self.wer.batch_dim_index, use_cer=self.wer.use_cer, log_prediction=self.wer.log_prediction, dist_sync_on_step=True, ) # Setup fused Joint step if self.joint.fuse_loss_wer or ( self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 ): self.joint.set_loss(self.loss) self.joint.set_wer(self.wer) # Update config with open_dict(self.cfg.joint): self.cfg.joint = new_joint_config with open_dict(self.cfg.decoder): self.cfg.decoder = new_decoder_config with open_dict(self.cfg.decoding): self.cfg.decoding = decoding_cfg 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.joint.vocabulary} vocabulary.") def change_decoding_strategy(self, decoding_cfg: DictConfig): """ Changes decoding strategy used during RNNT decoding process. Args: decoding_cfg: A config for the decoder, which is optional. If the decoding type needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. """ if decoding_cfg is None: # Assume same decoding config as before logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") decoding_cfg = self.cfg.decoding # Assert the decoding config with all hyper parameters decoding_cls = OmegaConf.structured(RNNTDecodingConfig) decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) self.decoding = RNNTDecoding( decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, ) self.wer = WER( decoding=self.decoding, batch_dim_index=self.wer.batch_dim_index, use_cer=self.wer.use_cer, log_prediction=self.wer.log_prediction, dist_sync_on_step=True, ) # Setup fused Joint step if self.joint.fuse_loss_wer or ( self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 ): self.joint.set_loss(self.loss) self.joint.set_wer(self.wer) # Update config with open_dict(self.cfg.decoding): self.cfg.decoding = decoding_cfg logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") def _setup_dataloader_from_config(self, config: Optional[Dict]): # 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') dataset = video_to_text_dataset.get_video_to_text_bpe_dataset_from_config( config=config, local_rank=self.local_rank, global_rank=self.global_rank, world_size=self.world_size, preprocessor_cfg=self._cfg.get("preprocessor", None), ) if dataset is None: return None shuffle = config['shuffle'] if config.get('is_tarred', False): shuffle = False 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.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` """ 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.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` """ 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.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` """ 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]]: return { "input_signal": NeuralType(('B', 'C', 'T', 'H', 'W'), VideoSignal(), optional=True), "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), } @property def output_types(self) -> Optional[Dict[str, NeuralType]]: return { "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), "encoded_lengths": NeuralType(tuple('B'), LengthsType()), } @typecheck() def forward(self, input_signal=None, input_signal_length=None): """ Forward pass of the model. Note that for RNNT Models, the forward pass of the model is a 3 step process, and this method only performs the first step - forward of the acoustic/visual model. Please refer to the `training_step` in order to see the full `forward` step for training - which performs the forward of the acoustic model, the prediction network and then the joint network. Finally, it computes the loss and possibly compute the detokenized text via the `decoding` step. Please refer to the `validation_step` in order to see the full `forward` step for inference - which performs the forward of the acoustic model, the prediction network and then the joint network. Finally, it computes the decoded tokens via the `decoding` step and possibly compute the batch metrics. Args: input_signal: Tensor that represents a batch of video signals, of shape [B, T, H, W, C]. T here represents timesteps, H height, W width and C channels input_signal_length: Vector of length B, that contains the individual lengths of the video sequences. Returns: A tuple of 2 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]. """ # Preprocessing processed_video_signal, processed_video_signal_length = self.video_preprocessor( input_signal=input_signal, length=input_signal_length ) # Augmentation processed_video_signal = self.video_augmentation( input_signal=processed_video_signal, length=processed_video_signal_length ) # Front-end Networks processed_video_signal, processed_video_signal_length = self.video_front_end( input_signal=processed_video_signal, length=processed_video_signal_length ) # Back-end Networks encoded, encoded_len = self.encoder(audio_signal=processed_video_signal, length=processed_video_signal_length) return encoded, encoded_len # PTL-specific methods def training_step(self, batch, batch_nb): # Reset access registry if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): AccessMixin.reset_registry(self) signal, signal_len, transcript, transcript_len = batch # forward() only performs encoder forward encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) del signal # During training, loss must be computed, so decoder forward is necessary decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) if hasattr(self, '_trainer') and self._trainer is not None: log_every_n_steps = self._trainer.log_every_n_steps sample_id = self._trainer.global_step else: log_every_n_steps = 1 sample_id = batch_nb # If experimental fused Joint-Loss-WER is not used if not self.joint.fuse_loss_wer: # Compute full joint and loss joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) loss_value = self.loss( log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length ) # Add auxiliary losses, if registered loss_value = self.add_auxiliary_losses(loss_value) # Reset access registry if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): AccessMixin.reset_registry(self) tensorboard_logs = { 'train_loss': loss_value, 'learning_rate': self._optimizer.param_groups[0]['lr'], 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), } if (sample_id + 1) % log_every_n_steps == 0: self.wer.update( predictions=encoded, predictions_lengths=encoded_len, targets=transcript, targets_lengths=transcript_len, ) _, scores, words = self.wer.compute() self.wer.reset() tensorboard_logs.update({'training_batch_wer': scores.float() / words}) else: # If experimental fused Joint-Loss-WER is used if (sample_id + 1) % log_every_n_steps == 0: compute_wer = True else: compute_wer = False # Fused joint step loss_value, wer, _, _ = self.joint( encoder_outputs=encoded, decoder_outputs=decoder, encoder_lengths=encoded_len, transcripts=transcript, transcript_lengths=transcript_len, compute_wer=compute_wer, ) # Add auxiliary losses, if registered loss_value = self.add_auxiliary_losses(loss_value) # Reset access registry if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): AccessMixin.reset_registry(self) tensorboard_logs = { 'train_loss': loss_value, 'learning_rate': self._optimizer.param_groups[0]['lr'], 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), } if compute_wer: tensorboard_logs.update({'training_batch_wer': wer}) # Log items self.log_dict(tensorboard_logs) # Preserve batch acoustic model T and language model U parameters if normalizing if self._optim_normalize_joint_txu: self._optim_normalize_txu = [encoded_len.max(), transcript_len.max()] return {'loss': loss_value} def predict_step(self, batch, batch_idx, dataloader_idx=0): signal, signal_len, transcript, transcript_len, sample_id = batch # forward() only performs encoder forward encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) del signal best_hyp_text = self.decoding.rnnt_decoder_predictions_tensor( encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False ) sample_id = sample_id.cpu().detach().numpy() return list(zip(sample_id, best_hyp_text)) def validation_step(self, batch, batch_idx, dataloader_idx=0): signal, signal_len, transcript, transcript_len = batch # forward() only performs encoder forward encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) del signal tensorboard_logs = {} # If experimental fused Joint-Loss-WER is not used if not self.joint.fuse_loss_wer: if self.compute_eval_loss: decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) loss_value = self.loss( log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length ) tensorboard_logs['val_loss'] = loss_value self.wer.update( predictions=encoded, predictions_lengths=encoded_len, targets=transcript, targets_lengths=transcript_len, ) wer, wer_num, wer_denom = self.wer.compute() self.wer.reset() tensorboard_logs['val_wer_num'] = wer_num tensorboard_logs['val_wer_denom'] = wer_denom tensorboard_logs['val_wer'] = wer else: # If experimental fused Joint-Loss-WER is used compute_wer = True if self.compute_eval_loss: decoded, target_len, states = self.decoder(targets=transcript, target_length=transcript_len) else: decoded = None target_len = transcript_len # Fused joint step loss_value, wer, wer_num, wer_denom = self.joint( encoder_outputs=encoded, decoder_outputs=decoded, encoder_lengths=encoded_len, transcripts=transcript, transcript_lengths=target_len, compute_wer=compute_wer, ) if loss_value is not None: tensorboard_logs['val_loss'] = loss_value tensorboard_logs['val_wer_num'] = wer_num tensorboard_logs['val_wer_denom'] = wer_denom tensorboard_logs['val_wer'] = wer self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) return tensorboard_logs def test_step(self, batch, batch_idx, dataloader_idx=0): logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) test_logs = { 'test_wer_num': logs['val_wer_num'], 'test_wer_denom': logs['val_wer_denom'], # 'test_wer': logs['val_wer'], } if 'val_loss' in logs: test_logs['test_loss'] = logs['val_loss'] return test_logs def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): if self.compute_eval_loss: val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() val_loss_log = {'val_loss': val_loss_mean} else: val_loss_log = {} wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum() wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum() tensorboard_logs = {**val_loss_log, 'val_wer': wer_num.float() / wer_denom} return {**val_loss_log, 'log': tensorboard_logs} def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): if self.compute_eval_loss: test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() test_loss_log = {'test_loss': test_loss_mean} else: test_loss_log = {} wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum() wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum() tensorboard_logs = {**test_loss_log, 'test_wer': wer_num.float() / wer_denom} return {**test_loss_log, 'log': tensorboard_logs} def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': """ Setup function for a temporary data loader which wraps the provided video file. Args: config: A python dictionary which contains the following keys: paths2video_files: (a list) of paths to video 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 video manifest is temporarily stored. Returns: A pytorch DataLoader for the given video 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['paths2video_files'])) dl_config = { 'manifest_filepath': manifest_filepath, 'labels': self.joint.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, } if config.get("augmentor"): dl_config['augmentor'] = config.get("augmentor") temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) return temporary_datalayer def on_after_backward(self): super().on_after_backward() if self._optim_variational_noise_std > 0 and self.global_step >= self._optim_variational_noise_start: for param_name, param in self.decoder.named_parameters(): if param.grad is not None: noise = torch.normal( mean=0.0, std=self._optim_variational_noise_std, size=param.size(), device=param.device, dtype=param.dtype, ) param.grad.data.add_(noise) if self._optim_normalize_joint_txu: T, U = self._optim_normalize_txu if T is not None and U is not None: for param_name, param in self.encoder.named_parameters(): if param.grad is not None: param.grad.data.div_(U) for param_name, param in self.decoder.named_parameters(): if param.grad is not None: param.grad.data.div_(T) if self._optim_normalize_encoder_norm: for param_name, param in self.encoder.named_parameters(): if param.grad is not None: norm = param.grad.norm() param.grad.data.div_(norm) if self._optim_normalize_decoder_norm: for param_name, param in self.decoder.named_parameters(): if param.grad is not None: norm = param.grad.norm() param.grad.data.div_(norm) if self._optim_normalize_joint_norm: for param_name, param in self.joint.named_parameters(): if param.grad is not None: norm = param.grad.norm() param.grad.data.div_(norm) # EncDecRNNTModel is exported in 2 parts def list_export_subnets(self): return ['encoder', 'decoder_joint'] # for export @property def decoder_joint(self): return RNNTDecoderJoint(self.decoder, self.joint) @classmethod def list_available_models(cls) -> List[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 = [] return results @property def wer(self): return self._wer @wer.setter def wer(self, wer): self._wer = wer