# Copyright (c) 2020, 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 from abc import abstractmethod from dataclasses import dataclass, field from math import ceil, floor from typing import Any, Dict, List, Optional, Union import torch from lightning.pytorch import Trainer from omegaconf import DictConfig, ListConfig, OmegaConf from torch.utils.data import DataLoader from torchmetrics import Accuracy from torchmetrics.regression import MeanAbsoluteError, MeanSquaredError from nemo.collections.asr.data import audio_to_label_dataset, feature_to_label_dataset from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel from nemo.collections.asr.models.label_models import EncDecSpeakerLabelModel from nemo.collections.asr.parts.mixins import TranscriptionMixin, TranscriptionReturnType from nemo.collections.asr.parts.mixins.transcription import InternalTranscribeConfig from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations from nemo.collections.common.losses import CrossEntropyLoss, MSELoss from nemo.collections.common.metrics import TopKClassificationAccuracy from nemo.core.classes.common import PretrainedModelInfo, typecheck from nemo.core.neural_types import * from nemo.utils import logging, model_utils from nemo.utils.cast_utils import cast_all __all__ = ['EncDecClassificationModel', 'EncDecRegressionModel'] @dataclass class ClassificationInferConfig: batch_size: int = 4 logprobs: bool = False _internal: InternalTranscribeConfig = field(default_factory=lambda: InternalTranscribeConfig()) @dataclass class RegressionInferConfig: batch_size: int = 4 logprobs: bool = True _internal: InternalTranscribeConfig = field(default_factory=lambda: InternalTranscribeConfig()) class _EncDecBaseModel(ASRModel, ExportableEncDecModel, TranscriptionMixin): """Encoder decoder Classification 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.num_nodes * trainer.num_devices # Convert config to a DictConfig cfg = model_utils.convert_model_config_to_dict_config(cfg) # Convert config to support Hydra 1.0+ instantiation cfg = model_utils.maybe_update_config_version(cfg) self.is_regression_task = cfg.get('is_regression_task', False) # Change labels if needed self._update_decoder_config(cfg.labels, cfg.decoder) super().__init__(cfg=cfg, trainer=trainer) if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None: self.spec_augmentation = ASRModel.from_config_dict(self._cfg.spec_augment) else: self.spec_augmentation = None if hasattr(self._cfg, 'crop_or_pad_augment') and self._cfg.crop_or_pad_augment is not None: self.crop_or_pad = ASRModel.from_config_dict(self._cfg.crop_or_pad_augment) else: self.crop_or_pad = None self.preprocessor = self._setup_preprocessor() self.encoder = self._setup_encoder() self.decoder = self._setup_decoder() self.loss = self._setup_loss() self._setup_metrics() @abstractmethod def _setup_preprocessor(self): """ Setup preprocessor for audio data Returns: Preprocessor """ pass @abstractmethod def _setup_encoder(self): """ Setup encoder for the Encoder-Decoder network Returns: Encoder """ pass @abstractmethod def _setup_decoder(self): """ Setup decoder for the Encoder-Decoder network Returns: Decoder """ pass @abstractmethod def _setup_loss(self): """ Setup loss function for training Returns: Loss function """ pass @abstractmethod def _setup_metrics(self): """ Setup metrics to be tracked in addition to loss Returns: void """ pass @property def input_types(self) -> Optional[Dict[str, NeuralType]]: if hasattr(self.preprocessor, '_sample_rate'): audio_eltype = AudioSignal(freq=self.preprocessor._sample_rate) else: audio_eltype = AudioSignal() return { "input_signal": NeuralType(('B', 'T'), audio_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 @abstractmethod def output_types(self) -> Optional[Dict[str, NeuralType]]: pass 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_length`` arguments." ) if not has_processed_signal: processed_signal, processed_signal_length = self.preprocessor( input_signal=input_signal, length=input_signal_length, ) # Crop or pad is always applied if self.crop_or_pad is not None: processed_signal, processed_signal_length = self.crop_or_pad( input_signal=processed_signal, length=processed_signal_length ) # Spec augment is not applied during evaluation/testing 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) logits = self.decoder(encoder_output=encoded) return logits 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=DictConfig(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 ( self._train_dl is not None and hasattr(self._train_dl, 'dataset') and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset) ): # 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=DictConfig(val_data_config)) def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]], use_feat: bool = False): 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) if use_feat and hasattr(self, '_setup_feature_label_dataloader'): self._test_dl = self._setup_feature_label_dataloader(config=DictConfig(test_data_config)) else: self._test_dl = self._setup_dataloader_from_config(config=DictConfig(test_data_config)) def test_dataloader(self): if self._test_dl is not None: return self._test_dl def _setup_dataloader_from_config(self, config: DictConfig): OmegaConf.set_struct(config, False) config.is_regression_task = self.is_regression_task OmegaConf.set_struct(config, True) if 'augmentor' in config: augmentor = process_augmentations(config['augmentor']) else: augmentor = None featurizer = WaveformFeaturizer( sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=augmentor ) shuffle = config['shuffle'] # 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` is None or " f"`tarred_audio_filepaths` is None. Provided config : {config}" ) return None if 'vad_stream' in config and config['vad_stream']: logging.warning("VAD inference does not support tarred dataset now") return None shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 dataset = audio_to_label_dataset.get_tarred_classification_label_dataset( featurizer=featurizer, config=config, shuffle_n=shuffle_n, global_rank=self.global_rank, world_size=self.world_size, ) shuffle = False batch_size = config['batch_size'] if hasattr(dataset, 'collate_fn'): collate_fn = dataset.collate_fn elif hasattr(dataset.datasets[0], 'collate_fn'): # support datasets that are lists of entries collate_fn = dataset.datasets[0].collate_fn else: # support datasets that are lists of lists collate_fn = dataset.datasets[0].datasets[0].collate_fn else: if 'manifest_filepath' in config and config['manifest_filepath'] is None: logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") return None if 'vad_stream' in config and config['vad_stream']: logging.info("Perform streaming frame-level VAD") dataset = audio_to_label_dataset.get_speech_label_dataset(featurizer=featurizer, config=config) batch_size = 1 collate_fn = dataset.vad_frame_seq_collate_fn else: dataset = audio_to_label_dataset.get_classification_label_dataset(featurizer=featurizer, config=config) batch_size = config['batch_size'] if hasattr(dataset, 'collate_fn'): collate_fn = dataset.collate_fn elif hasattr(dataset.datasets[0], 'collate_fn'): # support datasets that are lists of entries collate_fn = dataset.datasets[0].collate_fn else: # support datasets that are lists of lists collate_fn = dataset.datasets[0].datasets[0].collate_fn return torch.utils.data.DataLoader( dataset=dataset, batch_size=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_feature_label_dataloader(self, config: DictConfig) -> torch.utils.data.DataLoader: """ setup dataloader for VAD inference with audio features as input """ OmegaConf.set_struct(config, False) config.is_regression_task = self.is_regression_task OmegaConf.set_struct(config, True) if 'augmentor' in config: augmentor = process_augmentations(config['augmentor']) else: augmentor = None if 'manifest_filepath' in config and config['manifest_filepath'] is None: logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") return None dataset = feature_to_label_dataset.get_feature_label_dataset(config=config, augmentor=augmentor) if 'vad_stream' in config and config['vad_stream']: collate_func = dataset._vad_segment_collate_fn batch_size = 1 shuffle = False else: collate_func = dataset._collate_fn batch_size = config['batch_size'] shuffle = config['shuffle'] return torch.utils.data.DataLoader( dataset=dataset, batch_size=batch_size, collate_fn=collate_func, drop_last=config.get('drop_last', False), shuffle=shuffle, num_workers=config.get('num_workers', 0), pin_memory=config.get('pin_memory', False), ) @torch.no_grad() def transcribe( self, audio: Union[List[str], DataLoader], batch_size: int = 4, logprobs=None, override_config: Optional[ClassificationInferConfig] | Optional[RegressionInferConfig] = None, ) -> TranscriptionReturnType: """ Generate class labels for provided audio files. Use this method for debugging and prototyping. Args: audio: (a single or list) of paths to audio files or a np.ndarray audio array. Can also be a dataloader object that provides values that can be consumed by the model. Recommended length per file is approximately 1 second. 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 class labels. override_config: (Optional) ClassificationInferConfig to use for this inference call. If None, will use the default config. Returns: A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files """ if logprobs is None: logprobs = self.is_regression_task if override_config is None: if not self.is_regression_task: trcfg = ClassificationInferConfig(batch_size=batch_size, logprobs=logprobs) else: trcfg = RegressionInferConfig(batch_size=batch_size, logprobs=logprobs) else: if not isinstance(override_config, ClassificationInferConfig) and not isinstance( override_config, RegressionInferConfig ): raise ValueError( f"override_config must be of type {ClassificationInferConfig}, " f"but got {type(override_config)}" ) trcfg = override_config return super().transcribe(audio=audio, override_config=trcfg) """ Transcription related methods """ def _transcribe_input_manifest_processing( self, audio_files: List[str], temp_dir: str, trcfg: ClassificationInferConfig ): with open(os.path.join(temp_dir, 'manifest.json'), 'w', encoding='utf-8') as fp: for audio_file in audio_files: label = 0.0 if self.is_regression_task else self.cfg.labels[0] entry = {'audio_filepath': audio_file, 'duration': 100000.0, 'label': label} fp.write(json.dumps(entry) + '\n') config = {'paths2audio_files': audio_files, 'batch_size': trcfg.batch_size, 'temp_dir': temp_dir} return config def _transcribe_forward(self, batch: Any, trcfg: ClassificationInferConfig): logits = self.forward(input_signal=batch[0], input_signal_length=batch[1]) output = dict(logits=logits) return output def _transcribe_output_processing( self, outputs, trcfg: ClassificationInferConfig ) -> Union[List[str], List[torch.Tensor]]: logits = outputs.pop('logits') labels = [] if trcfg.logprobs: # dump log probs per file for idx in range(logits.shape[0]): lg = logits[idx] labels.append(lg.cpu().numpy()) else: labels_k = [] top_ks = self._accuracy.top_k for top_k_i in top_ks: # replace top k value with current top k self._accuracy.top_k = top_k_i labels_k_i = self._accuracy.top_k_predicted_labels(logits) labels_k_i = labels_k_i.cpu() labels_k.append(labels_k_i) # convenience: if only one top_k, pop out the nested list if len(top_ks) == 1: labels_k = labels_k[0] labels += labels_k # reset top k to orignal value self._accuracy.top_k = top_ks return labels 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: 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.cfg.labels, 'batch_size': min(config['batch_size'], len(config['paths2audio_files'])), 'trim_silence': False, 'shuffle': False, } temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) return temporary_datalayer @abstractmethod def _update_decoder_config(self, labels, cfg): pass @classmethod def get_transcribe_config(cls) -> ClassificationInferConfig: """ Utility method that returns the default config for transcribe() function. Returns: A dataclass """ return ClassificationInferConfig() class EncDecClassificationModel(EncDecSpeakerLabelModel, TranscriptionMixin): def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]], use_feat: bool = False): 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) if use_feat and hasattr(self, '_setup_feature_label_dataloader'): self._test_dl = self._setup_feature_label_dataloader(config=DictConfig(test_data_config)) else: self._test_dl = self._setup_dataloader_from_config(config=DictConfig(test_data_config)) def _setup_feature_label_dataloader(self, config: DictConfig) -> torch.utils.data.DataLoader: """ setup dataloader for VAD inference with audio features as input """ OmegaConf.set_struct(config, False) config.is_regression_task = self.is_regression_task OmegaConf.set_struct(config, True) if 'augmentor' in config: augmentor = process_augmentations(config['augmentor']) else: augmentor = None if 'manifest_filepath' in config and config['manifest_filepath'] is None: logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") return None dataset = feature_to_label_dataset.get_feature_label_dataset(config=config, augmentor=augmentor) if 'vad_stream' in config and config['vad_stream']: collate_func = dataset._vad_segment_collate_fn batch_size = 1 shuffle = False else: collate_func = dataset._collate_fn batch_size = config['batch_size'] shuffle = config['shuffle'] return torch.utils.data.DataLoader( dataset=dataset, batch_size=batch_size, collate_fn=collate_func, 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_dataloader_from_config(self, config: DictConfig): OmegaConf.set_struct(config, False) config.is_regression_task = self.is_regression_task OmegaConf.set_struct(config, True) if 'augmentor' in config: augmentor = process_augmentations(config['augmentor']) else: augmentor = None featurizer = WaveformFeaturizer( sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=augmentor ) shuffle = config['shuffle'] # 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` is None or " f"`tarred_audio_filepaths` is None. Provided config : {config}" ) return None if 'vad_stream' in config and config['vad_stream']: logging.warning("VAD inference does not support tarred dataset now") return None shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 dataset = audio_to_label_dataset.get_tarred_classification_label_dataset( featurizer=featurizer, config=config, shuffle_n=shuffle_n, global_rank=self.global_rank, world_size=self.world_size, ) shuffle = False batch_size = config['batch_size'] if hasattr(dataset, 'collate_fn'): collate_fn = dataset.collate_fn elif hasattr(dataset.datasets[0], 'collate_fn'): # support datasets that are lists of entries collate_fn = dataset.datasets[0].collate_fn else: # support datasets that are lists of lists collate_fn = dataset.datasets[0].datasets[0].collate_fn else: if 'manifest_filepath' in config and config['manifest_filepath'] is None: logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") return None if 'vad_stream' in config and config['vad_stream']: logging.info("Perform streaming frame-level VAD") dataset = audio_to_label_dataset.get_speech_label_dataset(featurizer=featurizer, config=config) batch_size = 1 collate_fn = dataset.vad_frame_seq_collate_fn else: dataset = audio_to_label_dataset.get_classification_label_dataset(featurizer=featurizer, config=config) batch_size = config['batch_size'] if hasattr(dataset, 'collate_fn'): collate_fn = dataset.collate_fn elif hasattr(dataset.datasets[0], 'collate_fn'): # support datasets that are lists of entries collate_fn = dataset.datasets[0].collate_fn else: # support datasets that are lists of lists collate_fn = dataset.datasets[0].datasets[0].collate_fn return torch.utils.data.DataLoader( dataset=dataset, batch_size=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 forward_for_export(self, audio_signal, length): encoded, length = self.encoder(audio_signal=audio_signal, length=length) logits = self.decoder(encoder_output=encoded, length=length) return logits def _update_decoder_config(self, labels, cfg): """ Update the number of classes in the decoder based on labels provided. Args: labels: The current labels of the model cfg: The config of the decoder which will be updated. """ OmegaConf.set_struct(cfg, False) if 'params' in cfg: cfg.params.num_classes = len(labels) cfg.num_classes = len(labels) OmegaConf.set_struct(cfg, True) def __init__(self, cfg: DictConfig, trainer: Trainer = None): logging.warning( "Please use the EncDecSpeakerLabelModel instead of this model. EncDecClassificationModel model is kept for backward compatibility with older models." ) self._update_decoder_config(cfg.labels, cfg.decoder) if hasattr(cfg, 'is_regression_task') and cfg.is_regression_task is not None: self.is_regression_task = cfg.is_regression_task else: self.is_regression_task = False super().__init__(cfg, trainer) if hasattr(cfg, 'crop_or_pad_augment') and cfg.crop_or_pad_augment is not None: self.crop_or_pad = ASRModel.from_config_dict(cfg.crop_or_pad_augment) else: self.crop_or_pad = None def change_labels(self, new_labels: List[str]): """ Changes labels used by the decoder model. 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 dataset. If new_labels == self.decoder.vocabulary then nothing will be changed. Args: new_labels: list with new labels. Must contain at least 2 elements. Typically, \ this is set of labels for the dataset. Returns: None """ if new_labels is not None and not isinstance(new_labels, ListConfig): new_labels = ListConfig(new_labels) if self._cfg.labels == new_labels: logging.warning( f"Old labels ({self._cfg.labels}) and new labels ({new_labels}) match. Not changing anything" ) else: if new_labels is None or len(new_labels) == 0: raise ValueError(f'New labels must be non-empty list of labels. But I got: {new_labels}') # Update config self._cfg.labels = new_labels decoder_config = self.decoder.to_config_dict() new_decoder_config = copy.deepcopy(decoder_config) self._update_decoder_config(new_labels, new_decoder_config) del self.decoder self.decoder = EncDecClassificationModel.from_config_dict(new_decoder_config) OmegaConf.set_struct(self._cfg.decoder, False) self._cfg.decoder = new_decoder_config OmegaConf.set_struct(self._cfg.decoder, True) if 'train_ds' in self._cfg and self._cfg.train_ds is not None: self._cfg.train_ds.labels = new_labels if 'validation_ds' in self._cfg and self._cfg.validation_ds is not None: self._cfg.validation_ds.labels = new_labels if 'test_ds' in self._cfg and self._cfg.test_ds is not None: self._cfg.test_ds.labels = new_labels logging.info(f"Changed decoder output to {self.decoder.num_classes} labels.") @classmethod def list_available_models(cls) -> Optional[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 = [] model = PretrainedModelInfo( pretrained_model_name="vad_multilingual_marblenet", description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_marblenet/versions/1.10.0/files/vad_multilingual_marblenet.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="vad_telephony_marblenet", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_telephony_marblenet/versions/1.0.0rc1/files/vad_telephony_marblenet.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="vad_marblenet", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_marblenet/versions/1.0.0rc1/files/vad_marblenet.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v1", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v1", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v1.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v1", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v1", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v1.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v2", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v2", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v2_subset_task", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2_subset_task", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2_subset_task.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v2_subset_task", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2_subset_task", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2_subset_task.nemo", ) results.append(model) return results 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: 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.cfg.labels, 'batch_size': min(config['batch_size'], len(config['paths2audio_files'])), 'trim_silence': False, 'shuffle': False, } temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) return temporary_datalayer @torch.no_grad() def transcribe( self, audio: Union[List[str], DataLoader], batch_size: int = 4, logprobs=None, override_config: Optional[ClassificationInferConfig] | Optional[RegressionInferConfig] = None, ) -> TranscriptionReturnType: """ Generate class labels for provided audio files. Use this method for debugging and prototyping. Args: audio: (a single or list) of paths to audio files or a np.ndarray audio array. Can also be a dataloader object that provides values that can be consumed by the model. Recommended length per file is approximately 1 second. 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 class labels. override_config: (Optional) ClassificationInferConfig to use for this inference call. If None, will use the default config. Returns: A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files """ if logprobs is None: logprobs = self.is_regression_task if override_config is None: if not self.is_regression_task: trcfg = ClassificationInferConfig(batch_size=batch_size, logprobs=logprobs) else: trcfg = RegressionInferConfig(batch_size=batch_size, logprobs=logprobs) else: if not isinstance(override_config, ClassificationInferConfig) and not isinstance( override_config, RegressionInferConfig ): raise ValueError( f"override_config must be of type {ClassificationInferConfig}, " f"but got {type(override_config)}" ) trcfg = override_config return super().transcribe(audio=audio, override_config=trcfg) """ Transcription related methods """ def _transcribe_input_manifest_processing( self, audio_files: List[str], temp_dir: str, trcfg: ClassificationInferConfig ): with open(os.path.join(temp_dir, 'manifest.json'), 'w', encoding='utf-8') as fp: for audio_file in audio_files: label = 0.0 if self.is_regression_task else self.cfg.labels[0] entry = {'audio_filepath': audio_file, 'duration': 100000.0, 'label': label} fp.write(json.dumps(entry) + '\n') config = {'paths2audio_files': audio_files, 'batch_size': trcfg.batch_size, 'temp_dir': temp_dir} return config def _transcribe_forward(self, batch: Any, trcfg: ClassificationInferConfig): logits = self.forward(input_signal=batch[0], input_signal_length=batch[1]) output = dict(logits=logits) return output def _transcribe_output_processing( self, outputs, trcfg: ClassificationInferConfig ) -> Union[List[str], List[torch.Tensor]]: logits = outputs.pop('logits') labels = [] if trcfg.logprobs: # dump log probs per file for idx in range(logits.shape[0]): lg = logits[idx] labels.append(lg.cpu().numpy()) else: labels_k = [] top_ks = self._accuracy.top_k for top_k_i in top_ks: # replace top k value with current top k self._accuracy.top_k = top_k_i labels_k_i = self._accuracy.top_k_predicted_labels(logits) labels_k_i = labels_k_i.cpu() labels_k.append(labels_k_i) # convenience: if only one top_k, pop out the nested list if len(top_ks) == 1: labels_k = labels_k[0] labels += labels_k # reset top k to orignal value self._accuracy.top_k = top_ks return labels def forward(self, input_signal, input_signal_length): logits, _ = super().forward(input_signal, input_signal_length) return logits class EncDecRegressionModel(_EncDecBaseModel): """Encoder decoder class for speech regression models. Model class creates training, validation methods for setting up data performing model forward pass. """ @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. """ result = [] return result def __init__(self, cfg: DictConfig, trainer: Trainer = None): if not cfg.get('is_regression_task', False): raise ValueError("EndDecRegressionModel requires the flag is_regression_task to be set as true") super().__init__(cfg=cfg, trainer=trainer) def _setup_preprocessor(self): return EncDecRegressionModel.from_config_dict(self._cfg.preprocessor) def _setup_encoder(self): return EncDecRegressionModel.from_config_dict(self._cfg.encoder) def _setup_decoder(self): return EncDecRegressionModel.from_config_dict(self._cfg.decoder) def _setup_loss(self): return MSELoss() def _setup_metrics(self): self._mse = MeanSquaredError() self._mae = MeanAbsoluteError() @property def output_types(self) -> Optional[Dict[str, NeuralType]]: return {"preds": NeuralType(tuple('B'), RegressionValuesType())} @typecheck() def forward(self, input_signal, input_signal_length): logits = super().forward(input_signal=input_signal, input_signal_length=input_signal_length) return logits.view(-1) # PTL-specific methods def training_step(self, batch, batch_idx): audio_signal, audio_signal_len, targets, targets_len = batch logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) loss = self.loss(preds=logits, labels=targets) train_mse = self._mse(preds=logits, target=targets) train_mae = self._mae(preds=logits, target=targets) self.log_dict( { 'train_loss': loss, 'train_mse': train_mse, 'train_mae': train_mae, 'learning_rate': self._optimizer.param_groups[0]['lr'], }, ) return {'loss': loss} def validation_step(self, batch, batch_idx, dataloader_idx: int = 0): audio_signal, audio_signal_len, targets, targets_len = batch logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) loss_value = self.loss(preds=logits, labels=targets) val_mse = self._mse(preds=logits, target=targets) val_mae = self._mae(preds=logits, target=targets) return {'val_loss': loss_value, 'val_mse': val_mse, 'val_mae': val_mae} def test_step(self, batch, batch_idx, dataloader_idx: int = 0): logs = self.validation_step(batch, batch_idx, dataloader_idx) return {'test_loss': logs['val_loss'], 'test_mse': logs['test_mse'], 'test_mae': logs['val_mae']} def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() val_mse = self._mse.compute() self._mse.reset() val_mae = self._mae.compute() self._mae.reset() tensorboard_logs = {'val_loss': val_loss_mean, 'val_mse': val_mse, 'val_mae': val_mae} return {'val_loss': val_loss_mean, 'val_mse': val_mse, 'val_mae': val_mae, 'log': tensorboard_logs} def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() test_mse = self._mse.compute() self._mse.reset() test_mae = self._mae.compute() self._mae.reset() tensorboard_logs = {'test_loss': test_loss_mean, 'test_mse': test_mse, 'test_mae': test_mae} return {'test_loss': test_loss_mean, 'test_mse': test_mse, 'test_mae': test_mae, 'log': tensorboard_logs} @torch.no_grad() def transcribe( self, audio: List[str], batch_size: int = 4, override_config: Optional[RegressionInferConfig] = None ) -> List[float]: """ Generate class labels for provided audio files. Use this method for debugging and prototyping. Args: paths2audio_files: (a list) of paths to audio files. \ Recommended length per file is approximately 1 second. batch_size: (int) batch size to use during inference. \ Bigger will result in better throughput performance but would use more memory. Returns: A list of predictions in the same order as paths2audio_files """ if override_config is None: trcfg = RegressionInferConfig(batch_size=batch_size, logprobs=True) else: if not isinstance(override_config, RegressionInferConfig): raise ValueError( f"override_config must be of type {RegressionInferConfig}, " f"but got {type(override_config)}" ) trcfg = override_config predictions = super().transcribe(audio, override_config=trcfg) return [float(pred) for pred in predictions] def _update_decoder_config(self, labels, cfg): OmegaConf.set_struct(cfg, False) if 'params' in cfg: cfg.params.num_classes = 1 else: cfg.num_classes = 1 OmegaConf.set_struct(cfg, True) class EncDecFrameClassificationModel(_EncDecBaseModel): """ EncDecFrameClassificationModel is a model that performs classification on each frame of the input audio. The default config (i.e., marblenet_3x2x64_20ms.yaml) outputs 20ms frames. """ def __init__(self, cfg: DictConfig, trainer: Trainer = None): self.num_classes = len(cfg.labels) self.eval_loop_cnt = 0 self.ratio_threshold = cfg.get('ratio_threshold', 0.2) if cfg.get("is_regression_task", False): raise ValueError("EndDecClassificationModel requires the flag is_regression_task to be set as false") super().__init__(cfg=cfg, trainer=trainer) self.decoder.output_types = self.output_types self.decoder.output_types_for_export = self.output_types @property def output_types(self) -> Optional[Dict[str, NeuralType]]: return {"outputs": NeuralType(('B', 'T', 'C'), LogitsType())} @classmethod def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: results = [] model = PretrainedModelInfo( pretrained_model_name="vad_multilingual_frame_marblenet", description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_frame_marblenet", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_frame_marblenet/versions/1.20.0/files/vad_multilingual_frame_marblenet.nemo", ) results.append(model) return results def _setup_preprocessor(self): return EncDecClassificationModel.from_config_dict(self._cfg.preprocessor) def _setup_encoder(self): return EncDecClassificationModel.from_config_dict(self._cfg.encoder) def _setup_decoder(self): return EncDecClassificationModel.from_config_dict(self._cfg.decoder) def _update_decoder_config(self, labels, cfg): """ Update the number of classes in the decoder based on labels provided. Args: labels: The current labels of the model cfg: The config of the decoder which will be updated. """ OmegaConf.set_struct(cfg, False) if 'params' in cfg: cfg.params.num_classes = len(labels) else: cfg.num_classes = len(labels) OmegaConf.set_struct(cfg, True) def _setup_metrics(self): self._accuracy = TopKClassificationAccuracy(dist_sync_on_step=True) self._macro_accuracy = Accuracy(num_classes=self.num_classes, average='macro', task="multiclass") def _setup_loss(self): if "loss" in self.cfg: weight = self.cfg.loss.get("weight", None) if weight in [None, "none", "None"]: weight = [1.0] * self.num_classes logging.info(f"Using cross-entropy with weights: {weight}") else: weight = [1.0] * self.num_classes return CrossEntropyLoss(logits_ndim=3, weight=weight) def _setup_dataloader_from_config(self, config: DictConfig): OmegaConf.set_struct(config, False) config.is_regression_task = self.is_regression_task OmegaConf.set_struct(config, True) shuffle = config.get('shuffle', False) 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 ): raise ValueError( "Could not load dataset as `manifest_filepath` is None or " f"`tarred_audio_filepaths` is None. Provided cfg : {config}" ) shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 dataset = audio_to_label_dataset.get_tarred_audio_multi_label_dataset( cfg=config, shuffle_n=shuffle_n, global_rank=self.global_rank, world_size=self.world_size, ) shuffle = False if hasattr(dataset, 'collate_fn'): collate_func = dataset.collate_fn else: collate_func = dataset.datasets[0].collate_fn else: if 'manifest_filepath' in config and config['manifest_filepath'] is None: raise ValueError(f"Could not load dataset as `manifest_filepath` is None. Provided cfg : {config}") dataset = audio_to_label_dataset.get_audio_multi_label_dataset(config) collate_func = dataset.collate_fn return torch.utils.data.DataLoader( dataset=dataset, batch_size=config.get("batch_size", 1), collate_fn=collate_func, 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_feature_label_dataloader(self, config: DictConfig) -> torch.utils.data.DataLoader: """ setup dataloader for VAD inference with audio features as input """ OmegaConf.set_struct(config, False) config.is_regression_task = self.is_regression_task OmegaConf.set_struct(config, True) if 'augmentor' in config: augmentor = process_augmentations(config['augmentor']) else: augmentor = None if 'manifest_filepath' in config and config['manifest_filepath'] is None: logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") return None dataset = feature_to_label_dataset.get_feature_multi_label_dataset(config=config, augmentor=augmentor) return torch.utils.data.DataLoader( dataset=dataset, batch_size=config.get("batch_size", 1), collate_fn=dataset.collate_fn, drop_last=config.get('drop_last', False), shuffle=config.get('shuffle', False), num_workers=config.get('num_workers', 0), pin_memory=config.get('pin_memory', False), ) def get_label_masks(self, labels, labels_len): mask = torch.arange(labels.size(1))[None, :].to(labels.device) < labels_len[:, None] return mask.to(labels.device, dtype=bool) @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_length`` arguments." ) if not has_processed_signal: processed_signal, processed_signal_length = self.preprocessor( input_signal=input_signal, length=input_signal_length, ) # Crop or pad is always applied if self.crop_or_pad is not None: processed_signal, processed_signal_length = self.crop_or_pad( input_signal=processed_signal, length=processed_signal_length ) # Spec augment is not applied during evaluation/testing 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) logits = self.decoder(encoded.transpose(1, 2)) return logits # PTL-specific methods def training_step(self, batch, batch_idx): audio_signal, audio_signal_len, labels, labels_len = batch logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) labels, labels_len = self.reshape_labels(logits, labels, audio_signal_len, labels_len) masks = self.get_label_masks(labels, labels_len) loss_value = self.loss(logits=logits, labels=labels, loss_mask=masks) 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), } metric_logits, metric_labels = self.get_metric_logits_labels(logits, labels, masks) self._accuracy(logits=metric_logits, labels=metric_labels) topk_scores = self._accuracy.compute() self._accuracy.reset() for top_k, score in zip(self._accuracy.top_k, topk_scores): tensorboard_logs[f'training_batch_accuracy_top@{top_k}'] = score return {'loss': loss_value, 'log': tensorboard_logs} def validation_step(self, batch, batch_idx, dataloader_idx: int = 0, tag: str = 'val'): audio_signal, audio_signal_len, labels, labels_len = batch logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) labels, labels_len = self.reshape_labels(logits, labels, audio_signal_len, labels_len) masks = self.get_label_masks(labels, labels_len) loss_value = self.loss(logits=logits, labels=labels, loss_mask=masks) metric_logits, metric_labels = self.get_metric_logits_labels(logits, labels, masks) acc = self._accuracy(logits=metric_logits, labels=metric_labels) correct_counts, total_counts = self._accuracy.correct_counts_k, self._accuracy.total_counts_k self._macro_accuracy.update(preds=metric_logits, target=metric_labels) stats = self._macro_accuracy._final_state() output = { f'{tag}_loss': loss_value, f'{tag}_correct_counts': correct_counts, f'{tag}_total_counts': total_counts, f'{tag}_acc_micro': acc, f'{tag}_acc_stats': stats, } if tag == 'val': if isinstance(self.trainer.val_dataloaders, (list, tuple)) and len(self.trainer.val_dataloaders) > 1: self.validation_step_outputs[dataloader_idx].append(output) else: self.validation_step_outputs.append(output) else: if isinstance(self.trainer.test_dataloaders, (list, tuple)) and len(self.trainer.test_dataloaders) > 1: self.test_step_outputs[dataloader_idx].append(output) else: self.test_step_outputs.append(output) return output def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0, tag: str = 'val'): val_loss_mean = torch.stack([x[f'{tag}_loss'] for x in outputs]).mean() correct_counts = torch.stack([x[f'{tag}_correct_counts'] for x in outputs]).sum(axis=0) total_counts = torch.stack([x[f'{tag}_total_counts'] for x in outputs]).sum(axis=0) self._accuracy.correct_counts_k = correct_counts self._accuracy.total_counts_k = total_counts topk_scores = self._accuracy.compute() self._macro_accuracy.tp = torch.stack([x[f'{tag}_acc_stats'][0] for x in outputs]).sum(axis=0) self._macro_accuracy.fp = torch.stack([x[f'{tag}_acc_stats'][1] for x in outputs]).sum(axis=0) self._macro_accuracy.tn = torch.stack([x[f'{tag}_acc_stats'][2] for x in outputs]).sum(axis=0) self._macro_accuracy.fn = torch.stack([x[f'{tag}_acc_stats'][3] for x in outputs]).sum(axis=0) macro_accuracy_score = self._macro_accuracy.compute() self._accuracy.reset() self._macro_accuracy.reset() tensorboard_log = { f'{tag}_loss': val_loss_mean, f'{tag}_acc_macro': macro_accuracy_score, } for top_k, score in zip(self._accuracy.top_k, topk_scores): tensorboard_log[f'{tag}_acc_micro_top@{top_k}'] = score self.log_dict(tensorboard_log, sync_dist=True) return tensorboard_log def test_step(self, batch, batch_idx, dataloader_idx=0): return self.validation_step(batch, batch_idx, dataloader_idx, tag='test') def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): return self.multi_validation_epoch_end(outputs, dataloader_idx, tag='test') def reshape_labels(self, logits, labels, logits_len, labels_len): """ Reshape labels to match logits shape. For example, each label is expected to cover a 40ms frame, while each frme prediction from the model covers 20ms. If labels are shorter than logits, labels are repeated, otherwise labels are folded and argmax is applied to obtain the label of each frame. When lengths of labels and logits are not factors of each other, labels are truncated or padded with zeros. The ratio_threshold=0.2 is used to determine whether to pad or truncate labels, where the value 0.2 is not important as in real cases the ratio is very close to either ceil(ratio) or floor(ratio). We use 0.2 here for easier unit-testing. This implementation does not allow frame length and label length that are not multiples of each other. Args: logits: logits tensor with shape [B, T1, C] labels: labels tensor with shape [B, T2] logits_len: logits length tensor with shape [B] labels_len: labels length tensor with shape [B] Returns: labels: labels tensor with shape [B, T1] labels_len: labels length tensor with shape [B] """ logits_max_len = logits.size(1) labels_max_len = labels.size(1) batch_size = logits.size(0) if logits_max_len < labels_max_len: ratio = labels_max_len // logits_max_len res = labels_max_len % logits_max_len if ceil(ratio) - ratio < self.ratio_threshold: # e.g., ratio is 1.99 # pad labels with zeros until labels_max_len is a multiple of logits_max_len labels = labels.cpu().tolist() if len(labels) % ceil(ratio) != 0: labels += [0] * (ceil(ratio) - len(labels) % ceil(ratio)) labels = torch.tensor(labels).long().to(logits.device) labels = labels.view(-1, ceil(ratio)).amax(1) return self.reshape_labels(logits, labels, logits_len, labels_len) else: # truncate additional labels until labels_max_len is a multiple of logits_max_len if res > 0: labels = labels[:, :-res] mask = labels_len > (labels_max_len - res) labels_len = labels_len - mask * (labels_len - (labels_max_len - res)) labels = labels.view(batch_size, ratio, -1).amax(1) labels_len = torch.div(labels_len, ratio, rounding_mode="floor") labels_len = torch.min(torch.cat([logits_len[:, None], labels_len[:, None]], dim=1), dim=1)[0] return labels.contiguous(), labels_len.contiguous() elif logits_max_len > labels_max_len: ratio = logits_max_len / labels_max_len res = logits_max_len % labels_max_len if ceil(ratio) - ratio < self.ratio_threshold: # e.g., ratio is 1.99 # repeat labels for ceil(ratio) times, and DROP additional labels based on logits_max_len labels = labels.repeat_interleave(ceil(ratio), dim=1).long() labels = labels[:, :logits_max_len] labels_len = labels_len * ceil(ratio) mask = labels_len > logits_max_len labels_len = labels_len - mask * (labels_len - logits_max_len) else: # e.g., ratio is 2.01 # repeat labels for floor(ratio) times, and ADD padding labels based on logits_max_len labels = labels.repeat_interleave(floor(ratio), dim=1).long() labels_len = labels_len * floor(ratio) if res > 0: labels = torch.cat([labels, labels[:, -res:]], dim=1) # no need to update `labels_len` since we ignore additional "res" padded labels labels_len = torch.min(torch.cat([logits_len[:, None], labels_len[:, None]], dim=1), dim=1)[0] return labels.contiguous(), labels_len.contiguous() else: labels_len = torch.min(torch.cat([logits_len[:, None], labels_len[:, None]], dim=1), dim=1)[0] return labels, labels_len def get_metric_logits_labels(self, logits, labels, masks): """ Computes valid logits and labels for metric computation. Args: logits: tensor of shape [B, T, C] labels: tensor of shape [B, T] masks: tensor of shape [B, T] Returns: logits of shape [N, C] labels of shape [N,] """ C = logits.size(2) logits = logits.view(-1, C) # [BxT, C] labels = labels.view(-1).contiguous() # [BxT,] masks = masks.view(-1) # [BxT,] idx = masks.nonzero() # [BxT, 1] logits = logits.gather(dim=0, index=idx.repeat(1, 2)) labels = labels.gather(dim=0, index=idx.view(-1)) return logits, labels def forward_for_export( self, input, length=None, cache_last_channel=None, cache_last_time=None, cache_last_channel_len=None ): """ This forward is used when we need to export the model to ONNX format. Inputs cache_last_channel and cache_last_time are needed to be passed for exporting streaming models. Args: input: Tensor that represents a batch of raw audio signals, of shape [B, T]. T here represents timesteps. length: Vector of length B, that contains the individual lengths of the audio sequences. cache_last_channel: Tensor of shape [N, B, T, H] which contains the cache for last channel layers cache_last_time: Tensor of shape [N, B, H, T] which contains the cache for last time layers N is the number of such layers which need caching, B is batch size, H is the hidden size of activations, and T is the length of the cache Returns: the output of the model """ enc_fun = getattr(self.input_module, 'forward_for_export', self.input_module.forward) if cache_last_channel is None: encoder_output = enc_fun(audio_signal=input, length=length) if isinstance(encoder_output, tuple): encoder_output = encoder_output[0] else: encoder_output, length, cache_last_channel, cache_last_time, cache_last_channel_len = enc_fun( audio_signal=input, length=length, cache_last_channel=cache_last_channel, cache_last_time=cache_last_time, cache_last_channel_len=cache_last_channel_len, ) dec_fun = getattr(self.output_module, 'forward_for_export', self.output_module.forward) ret = dec_fun(hidden_states=encoder_output.transpose(1, 2)) if isinstance(ret, tuple): ret = ret[0] if cache_last_channel is not None: ret = (ret, length, cache_last_channel, cache_last_time, cache_last_channel_len) return cast_all(ret, from_dtype=torch.float16, to_dtype=torch.float32)