# 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, 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.ctc import CTCLoss from nemo.collections.asr.metrics.wer import WER from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel from nemo.collections.asr.parts.mixins import ASRModuleMixin, InterCTCMixin from nemo.collections.asr.parts.preprocessing.segment import ChannelSelectorType from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset from nemo.core.classes.common import PretrainedModelInfo, typecheck from nemo.core.classes.mixins import AccessMixin from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, NeuralType, VideoSignal from nemo.utils import logging __all__ = ['VisualEncDecCTCModel'] class VisualEncDecCTCModel(ASRModel, ExportableEncDecModel, ASRModuleMixin, InterCTCMixin): """Base class for encoder decoder CTC-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 # Init super().__init__(cfg=cfg, trainer=trainer) # Preprocessor, video transforms self.video_preprocessor = VisualEncDecCTCModel.from_config_dict(self._cfg.video_preprocessor) # Augmentation, video augmentations self.video_augmentation = VisualEncDecCTCModel.from_config_dict(self._cfg.video_augment) # Front-end Network, learned module that transform videos to temporal sequence self.video_front_end = VisualEncDecCTCModel.from_config_dict(self._cfg.video_front_end) # Encoder Network self.encoder = VisualEncDecCTCModel.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) # Decoder self.decoder = VisualEncDecCTCModel.from_config_dict(self._cfg.decoder) # CTC Loss self.loss = CTCLoss( num_classes=self.decoder.num_classes_with_blank - 1, zero_infinity=True, reduction=self._cfg.get("ctc_reduction", "mean_batch"), ) # Setup decoding objects decoding_cfg = self.cfg.get('decoding', None) # In case decoding config not found, use default config if decoding_cfg is None: decoding_cfg = OmegaConf.structured(CTCDecodingConfig) with open_dict(self.cfg): self.cfg.decoding = decoding_cfg # Decoding self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=self.decoder.vocabulary) # Setup metric with decoding strategy self.wer = WER( decoding=self.decoding, use_cer=self._cfg.get('use_cer', False), dist_sync_on_step=True, log_prediction=self._cfg.get("log_prediction", False), ) # Setup optional Optimization flags self.setup_optimization_flags() # setting up interCTC loss (from InterCTCMixin) self.setup_interctc(decoder_name='decoder', loss_name='loss', wer_name='_wer') # Adapter modules setup (from ASRAdapterModelMixin) self.setup_adapters() @torch.no_grad() def transcribe( self, paths2video_files: List[str], batch_size: int = 4, logprobs: bool = False, return_hypotheses: bool = False, num_workers: int = 0, channel_selector: Optional[ChannelSelectorType] = None, augmentor: DictConfig = None, ) -> List[str]: """ 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. 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 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`. augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. Returns: A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2video_files """ if paths2video_files is None or len(paths2video_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 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() 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"): 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.decoding.ctc_decoder_predictions_tensor( logits, decoder_lengths=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]] if current_hypotheses[idx].alignments is None: current_hypotheses[idx].alignments = current_hypotheses[idx].y_sequence hypotheses += current_hypotheses del greedy_predictions del logits del test_batch finally: # set mode back to its original value self.train(mode=mode) if mode is True: self.video_front_end.unfreeze() self.encoder.unfreeze() self.decoder.unfreeze() logging.set_verbosity(logging_level) return hypotheses def change_vocabulary(self, new_vocabulary: List[str], decoding_cfg: Optional[DictConfig] = None): """ 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 = VisualEncDecCTCModel.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"), ) 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(CTCDecodingConfig) decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) self.decoding = CTCDecoding(decoding_cfg=decoding_cfg, vocabulary=self.decoder.vocabulary) self.wer = WER( decoding=self.decoding, use_cer=self._cfg.get('use_cer', False), dist_sync_on_step=True, log_prediction=self._cfg.get("log_prediction", False), ) # Update 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.decoder.vocabulary} vocabulary.") def change_decoding_strategy(self, decoding_cfg: DictConfig): """ Changes decoding strategy used during CTC 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(CTCDecodingConfig) decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) self.decoding = CTCDecoding(decoding_cfg=decoding_cfg, vocabulary=self.decoder.vocabulary) self.wer = WER( decoding=self.decoding, use_cer=self.wer.use_cer, log_prediction=self.wer.log_prediction, dist_sync_on_step=True, ) # 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_char_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_video_signal": NeuralType(('B', 'C', 'T', 'H', 'W'), VideoSignal(), optional=True), "input_video_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_video_signal=None, input_video_signal_length=None): """ Forward pass of the model. Args: input_video_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_video_signal_length: Vector of length B, that contains the individual lengths of the video 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) """ # Preprocessing processed_video_signal, processed_video_signal_length = self.video_preprocessor( input_signal=input_video_signal, length=input_video_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) 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): # Reset access registry if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): AccessMixin.reset_registry(self) if self.is_interctc_enabled(): AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) video_signal, video_signal_len, transcript, transcript_len = batch log_probs, encoded_len, predictions = self.forward( input_video_signal=video_signal, input_video_signal_length=video_signal_len ) 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 loss_value = self.loss( log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len ) # Add auxiliary losses, if registered loss_value = self.add_auxiliary_losses(loss_value) # only computing WER when requested in the logs (same as done for final-layer WER below) loss_value, tensorboard_logs = self.add_interctc_losses( loss_value, transcript, transcript_len, compute_wer=((batch_nb + 1) % log_every_n_steps == 0) ) # Reset access registry if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): AccessMixin.reset_registry(self) tensorboard_logs.update( { 'train_loss': loss_value, 'learning_rate': self._optimizer.param_groups[0]['lr'], 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), } ) if (batch_nb + 1) % log_every_n_steps == 0: self.wer.update( predictions=log_probs, 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): video_signal, video_signal_len, transcript, transcript_len, sample_id = batch log_probs, encoded_len, predictions = self.forward( input_video_signal=video_signal, input_video_signal_length=video_signal_len ) transcribed_texts, _ = self.wer.decoding.ctc_decoder_predictions_tensor( decoder_outputs=log_probs, decoder_lengths=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): if self.is_interctc_enabled(): AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) video_signal, video_signal_len, transcript, transcript_len = batch log_probs, encoded_len, predictions = self.forward( input_video_signal=video_signal, input_video_signal_length=video_signal_len ) loss_value = self.loss( log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len ) loss_value, metrics = self.add_interctc_losses( loss_value, transcript, transcript_len, compute_wer=True, log_wer_num_denom=True, log_prefix="val_", ) self.wer.update( predictions=log_probs, targets=transcript, target_lengths=transcript_len, predictions_lengths=encoded_len ) wer, wer_num, wer_denom = self.wer.compute() self.wer.reset() metrics.update({'val_loss': loss_value, 'val_wer_num': wer_num, 'val_wer_denom': wer_denom, 'val_wer': wer}) self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) # Reset access registry if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): AccessMixin.reset_registry(self) return metrics def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): metrics = super().multi_validation_epoch_end(outputs, dataloader_idx) self.finalize_interctc_metrics(metrics, outputs, prefix="val_") return metrics def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): metrics = super().multi_test_epoch_end(outputs, dataloader_idx) self.finalize_interctc_metrics(metrics, outputs, prefix="test_") return metrics def test_step(self, batch, batch_idx, dataloader_idx=0): logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) test_logs = {name.replace("val_", "test_"): value for name, value in logs.items()} if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: self.test_step_outputs[dataloader_idx].append(test_logs) else: self.test_step_outputs.append(test_logs) 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 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. 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 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.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, 'channel_selector': config.get('channel_selector', None), } if config.get("augmentor"): dl_config['augmentor'] = config.get("augmentor") temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) return temporary_datalayer @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