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