class EncDecRNNTModel(ASRModel, ASRModuleMixin):
"""Base class for encoder decoder RNNT-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 = []
return result
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_gpus
super().__init__(cfg=cfg, trainer=trainer)
# Initialize components
self.preprocessor = EncDecRNNTModel.from_config_dict(
self.cfg.preprocessor)
self.encoder = EncDecRNNTModel.from_config_dict(self.cfg.encoder)
# Update config values required by components dynamically
with open_dict(self.cfg.decoder):
self.cfg.decoder.vocab_size = len(self.cfg.labels)
with open_dict(self.cfg.joint):
self.cfg.joint.num_classes = len(self.cfg.labels)
self.cfg.joint.vocabulary = self.cfg.labels
self.cfg.joint.jointnet.encoder_hidden = self.cfg.model_defaults.enc_hidden
self.cfg.joint.jointnet.pred_hidden = self.cfg.model_defaults.pred_hidden
self.decoder = EncDecRNNTModel.from_config_dict(self.cfg.decoder)
self.joint = EncDecRNNTModel.from_config_dict(self.cfg.joint)
# Setup RNNT Loss
loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(
self.cfg.get("loss", None))
self.loss = RNNTLoss(num_classes=self.joint.num_classes_with_blank - 1,
loss_name=loss_name,
loss_kwargs=loss_kwargs)
if hasattr(self.cfg,
'spec_augment') and self._cfg.spec_augment is not None:
self.spec_augmentation = EncDecRNNTModel.from_config_dict(
self.cfg.spec_augment)
else:
self.spec_augmentation = None
# Setup decoding objects
self.decoding = RNNTDecoding(
decoding_cfg=self.cfg.decoding,
decoder=self.decoder,
joint=self.joint,
vocabulary=self.joint.vocabulary,
)
# Setup WER calculation
self.wer = RNNTWER(
decoding=self.decoding,
batch_dim_index=0,
use_cer=self._cfg.get('use_cer', False),
log_prediction=self._cfg.get('log_prediction', True),
dist_sync_on_step=True,
)
# Whether to compute loss during evaluation
if 'compute_eval_loss' in self.cfg:
self.compute_eval_loss = self.cfg.compute_eval_loss
else:
self.compute_eval_loss = True
# Setup fused Joint step if flag is set
if self.joint.fuse_loss_wer:
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
self.setup_optim_normalization()
def setup_optim_normalization(self):
"""
Helper method to setup normalization of certain parts of the model prior to the optimization step.
Supported pre-optimization normalizations are as follows:
.. code-block:: yaml
# Variation Noise injection
model:
variational_noise:
std: 0.0
start_step: 0
# Joint - Length normalization
model:
normalize_joint_txu: false
# Encoder Network - gradient normalization
model:
normalize_encoder_norm: false
# Decoder / Prediction Network - gradient normalization
model:
normalize_decoder_norm: false
# Joint - gradient normalization
model:
normalize_joint_norm: false
"""
# setting up the variational noise for the decoder
if hasattr(self.cfg, 'variational_noise'):
self._optim_variational_noise_std = self.cfg[
'variational_noise'].get('std', 0)
self._optim_variational_noise_start = self.cfg[
'variational_noise'].get('start_step', 0)
else:
self._optim_variational_noise_std = 0
self._optim_variational_noise_start = 0
# Setup normalized gradients for model joint by T x U scaling factor (joint length normalization)
self._optim_normalize_joint_txu = self.cfg.get('normalize_joint_txu',
False)
self._optim_normalize_txu = None
# Setup normalized encoder norm for model
self._optim_normalize_encoder_norm = self.cfg.get(
'normalize_encoder_norm', False)
# Setup normalized decoder norm for model
self._optim_normalize_decoder_norm = self.cfg.get(
'normalize_decoder_norm', False)
# Setup normalized joint norm for model
self._optim_normalize_joint_norm = self.cfg.get(
'normalize_joint_norm', False)
def extract_rnnt_loss_cfg(self, cfg: Optional[DictConfig]):
"""
Helper method to extract the rnnt loss name, and potentially its kwargs
to be passed.
Args:
cfg: Should contain `loss_name` as a string which is resolved to a RNNT loss name.
If the default should be used, then `default` can be used.
Optionally, one can pass additional kwargs to the loss function. The subdict
should have a keyname as follows : `{loss_name}_kwargs`.
Note that whichever loss_name is selected, that corresponding kwargs will be
selected. For the "default" case, the "{resolved_default}_kwargs" will be used.
Examples:
.. code-block:: yaml
loss_name: "default"
warprnnt_numba_kwargs:
kwargs2: some_other_val
Returns:
A tuple, the resolved loss name as well as its kwargs (if found).
"""
if cfg is None:
cfg = DictConfig({})
loss_name = cfg.get("loss_name", "default")
if loss_name == "default":
loss_name = resolve_rnnt_default_loss_name()
loss_kwargs = cfg.get(f"{loss_name}_kwargs", None)
logging.info(f"Using RNNT Loss : {loss_name}\n"
f"Loss {loss_name}_kwargs: {loss_kwargs}")
return loss_name, loss_kwargs
@torch.no_grad()
def transcribe(self,
paths2audio_files: List[str],
batch_size: int = 4,
return_hypotheses: bool = 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.
return_hypotheses: (bool) Either return hypotheses or text
With hypotheses can do some postprocessing like getting timestamp or rescoring
Returns:
A list of transcriptions 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
try:
# Switch model to evaluation mode
self.eval()
# Freeze the encoder and decoder modules
self.encoder.freeze()
self.decoder.freeze()
self.joint.freeze()
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
# Work in tmp directory - will store manifest file there
with tempfile.TemporaryDirectory() as tmpdir:
with open(os.path.join(tmpdir, 'manifest.json'), 'w') 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 tqdm(temporary_datalayer,
desc="Transcribing"):
encoded, encoded_len = self.forward(
input_signal=test_batch[0].to(device),
input_signal_length=test_batch[1].to(device))
hypotheses += self.decoding.rnnt_decoder_predictions_tensor(
encoded,
encoded_len,
return_hypotheses=return_hypotheses)
del encoded
del test_batch
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
if mode is True:
self.encoder.unfreeze()
self.decoder.unfreeze()
self.joint.unfreeze()
return hypotheses
def change_vocabulary(self,
new_vocabulary: List[str],
decoding_cfg: Optional[DictConfig] = None):
"""
Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning a pre-trained model.
This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would
use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need
model to learn capitalization, punctuation and/or special characters.
Args:
new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \
this is target alphabet.
decoding_cfg: A config for the decoder, which is optional. If the decoding type
needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here.
Returns: None
"""
if self.joint.vocabulary == new_vocabulary:
logging.warning(
f"Old {self.joint.vocabulary} and new {new_vocabulary} match. Not changing anything."
)
else:
if new_vocabulary is None or len(new_vocabulary) == 0:
raise ValueError(
f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}'
)
joint_config = self.joint.to_config_dict()
new_joint_config = copy.deepcopy(joint_config)
new_joint_config['vocabulary'] = new_vocabulary
new_joint_config['num_classes'] = len(new_vocabulary)
del self.joint
self.joint = EncDecRNNTModel.from_config_dict(new_joint_config)
decoder_config = self.decoder.to_config_dict()
new_decoder_config = copy.deepcopy(decoder_config)
new_decoder_config.vocab_size = len(new_vocabulary)
del self.decoder
self.decoder = EncDecRNNTModel.from_config_dict(new_decoder_config)
del self.loss
loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(
self.cfg.get('loss', None))
self.loss = RNNTLoss(
num_classes=self.joint.num_classes_with_blank - 1,
loss_name=loss_name,
loss_kwargs=loss_kwargs)
if decoding_cfg is None:
# Assume same decoding config as before
decoding_cfg = self.cfg.decoding
self.decoding = RNNTDecoding(
decoding_cfg=decoding_cfg,
decoder=self.decoder,
joint=self.joint,
vocabulary=self.joint.vocabulary,
)
self.wer = RNNTWER(
decoding=self.decoding,
batch_dim_index=self.wer.batch_dim_index,
use_cer=self.wer.use_cer,
log_prediction=self.wer.log_prediction,
dist_sync_on_step=True,
)
# Setup fused Joint step
if self.joint.fuse_loss_wer:
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
# Update config
with open_dict(self.cfg.joint):
self.cfg.joint = new_joint_config
with open_dict(self.cfg.decoder):
self.cfg.decoder = new_decoder_config
with open_dict(self.cfg.decoding):
self.cfg.decoding = decoding_cfg
ds_keys = ['train_ds', 'validation_ds', 'test_ds']
for key in ds_keys:
if key in self.cfg:
with open_dict(self.cfg[key]):
self.cfg[key]['labels'] = OmegaConf.create(
new_vocabulary)
logging.info(
f"Changed decoder to output to {self.joint.vocabulary} vocabulary."
)
def change_decoding_strategy(self, decoding_cfg: DictConfig):
"""
Changes decoding strategy used during RNNT decoding process.
Args:
decoding_cfg: A config for the decoder, which is optional. If the decoding type
needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here.
"""
if decoding_cfg is None:
# Assume same decoding config as before
logging.info(
"No `decoding_cfg` passed when changing decoding strategy, using internal config"
)
decoding_cfg = self.cfg.decoding
self.decoding = RNNTDecoding(
decoding_cfg=decoding_cfg,
decoder=self.decoder,
joint=self.joint,
vocabulary=self.joint.vocabulary,
)
self.wer = RNNTWER(
decoding=self.decoding,
batch_dim_index=self.wer.batch_dim_index,
use_cer=self.wer.use_cer,
log_prediction=self.wer.log_prediction,
dist_sync_on_step=True,
)
# Setup fused Joint step
if self.joint.fuse_loss_wer:
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
# Update config
with open_dict(self.cfg.decoding):
self.cfg.decoding = decoding_cfg
logging.info(
f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}"
)
def _setup_dataloader_from_config(self, config: Optional[Dict]):
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_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]]):
"""
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 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]]):
"""
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),
}
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {
"outputs": NeuralType(('B', 'D', 'T'),
AcousticEncodedRepresentation()),
"encoded_lengths": NeuralType(tuple('B'), LengthsType()),
}
@typecheck()
def forward(self,
input_signal=None,
input_signal_length=None,
processed_signal=None,
processed_signal_length=None):
"""
Forward pass of the model. Note that for RNNT Models, the forward pass of the model is a 3 step process,
and this method only performs the first step - forward of the acoustic model.
Please refer to the `training_step` in order to see the full `forward` step for training - which
performs the forward of the acoustic model, the prediction network and then the joint network.
Finally, it computes the loss and possibly compute the detokenized text via the `decoding` step.
Please refer to the `validation_step` in order to see the full `forward` step for inference - which
performs the forward of the acoustic model, the prediction network and then the joint network.
Finally, it computes the decoded tokens via the `decoding` step and possibly compute the batch metrics.
Args:
input_signal: Tensor that represents a batch of 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 2 elements -
1) The log probabilities tensor of shape [B, T, D].
2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B].
"""
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) is 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,
)
# 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)
return encoded, encoded_len
# PTL-specific methods
def training_step(self, batch, batch_nb):
signal, signal_len, transcript, transcript_len = batch
# forward() only performs encoder forward
if isinstance(batch, DALIOutputs) and batch.has_processed_signal:
encoded, encoded_len = self.forward(
processed_signal=signal, processed_signal_length=signal_len)
else:
encoded, encoded_len = self.forward(input_signal=signal,
input_signal_length=signal_len)
del signal
# During training, loss must be computed, so decoder forward is necessary
decoder, target_length = self.decoder(targets=transcript,
target_length=transcript_len)
if hasattr(self, '_trainer') and self._trainer is not None:
log_every_n_steps = self._trainer.log_every_n_steps
sample_id = self._trainer.global_step
else:
log_every_n_steps = 1
sample_id = batch_nb
# If experimental fused Joint-Loss-WER is not used
if not self.joint.fuse_loss_wer:
# Compute full joint and loss
joint = self.joint(encoder_outputs=encoded,
decoder_outputs=decoder)
loss_value = self.loss(log_probs=joint,
targets=transcript,
input_lengths=encoded_len,
target_lengths=target_length)
tensorboard_logs = {
'train_loss': loss_value,
'learning_rate': self._optimizer.param_groups[0]['lr']
}
if (sample_id + 1) % log_every_n_steps == 0:
self.wer.update(encoded, encoded_len, transcript,
transcript_len)
_, scores, words = self.wer.compute()
self.wer.reset()
tensorboard_logs.update(
{'training_batch_wer': scores.float() / words})
else:
# If experimental fused Joint-Loss-WER is used
if (sample_id + 1) % log_every_n_steps == 0:
compute_wer = True
else:
compute_wer = False
# Fused joint step
loss_value, wer, _, _ = self.joint(
encoder_outputs=encoded,
decoder_outputs=decoder,
encoder_lengths=encoded_len,
transcripts=transcript,
transcript_lengths=transcript_len,
compute_wer=compute_wer,
)
tensorboard_logs = {
'train_loss': loss_value,
'learning_rate': self._optimizer.param_groups[0]['lr']
}
if compute_wer:
tensorboard_logs.update({'training_batch_wer': wer})
# Log items
self.log_dict(tensorboard_logs)
# Preserve batch acoustic model T and language model U parameters if normalizing
if self._optim_normalize_joint_txu:
self._optim_normalize_txu = [
encoded_len.max(), transcript_len.max()
]
return {'loss': loss_value}
def validation_step(self, batch, batch_idx, dataloader_idx=0):
signal, signal_len, transcript, transcript_len = batch
# forward() only performs encoder forward
if isinstance(batch, DALIOutputs) and batch.has_processed_signal:
encoded, encoded_len = self.forward(
processed_signal=signal, processed_signal_length=signal_len)
else:
encoded, encoded_len = self.forward(input_signal=signal,
input_signal_length=signal_len)
del signal
tensorboard_logs = {}
# If experimental fused Joint-Loss-WER is not used
if not self.joint.fuse_loss_wer:
if self.compute_eval_loss:
decoder, target_length = self.decoder(
targets=transcript, target_length=transcript_len)
joint = self.joint(encoder_outputs=encoded,
decoder_outputs=decoder)
loss_value = self.loss(log_probs=joint,
targets=transcript,
input_lengths=encoded_len,
target_lengths=target_length)
tensorboard_logs['val_loss'] = loss_value
self.wer.update(encoded, encoded_len, transcript, transcript_len)
wer, wer_num, wer_denom = self.wer.compute()
self.wer.reset()
tensorboard_logs['val_wer_num'] = wer_num
tensorboard_logs['val_wer_denom'] = wer_denom
tensorboard_logs['val_wer'] = wer
else:
# If experimental fused Joint-Loss-WER is used
compute_wer = True
if self.compute_eval_loss:
decoded, target_len = self.decoder(
targets=transcript, target_length=transcript_len)
else:
decoded = None
target_len = transcript_len
# Fused joint step
loss_value, wer, wer_num, wer_denom = self.joint(
encoder_outputs=encoded,
decoder_outputs=decoded,
encoder_lengths=encoded_len,
transcripts=transcript,
transcript_lengths=target_len,
compute_wer=compute_wer,
)
if loss_value is not None:
tensorboard_logs['val_loss'] = loss_value
tensorboard_logs['val_wer_num'] = wer_num
tensorboard_logs['val_wer_denom'] = wer_denom
tensorboard_logs['val_wer'] = wer
return tensorboard_logs
def test_step(self, batch, batch_idx, dataloader_idx=0):
logs = self.validation_step(batch,
batch_idx,
dataloader_idx=dataloader_idx)
test_logs = {
'test_wer_num': logs['val_wer_num'],
'test_wer_denom': logs['val_wer_denom'],
# 'test_wer': logs['val_wer'],
}
if 'val_loss' in logs:
test_logs['test_loss'] = logs['val_loss']
return test_logs
def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0):
if self.compute_eval_loss:
val_loss_mean = torch.stack([x['val_loss']
for x in outputs]).mean()
val_loss_log = {'val_loss': val_loss_mean}
else:
val_loss_log = {}
wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum()
wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum()
tensorboard_logs = {
**val_loss_log, 'val_wer': wer_num.float() / wer_denom
}
return {**val_loss_log, 'log': tensorboard_logs}
def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0):
if self.compute_eval_loss:
test_loss_mean = torch.stack([x['test_loss']
for x in outputs]).mean()
test_loss_log = {'test_loss': test_loss_mean}
else:
test_loss_log = {}
wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum()
wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum()
tensorboard_logs = {
**test_loss_log, 'test_wer': wer_num.float() / wer_denom
}
return {**test_loss_log, 'log': tensorboard_logs}
def _setup_transcribe_dataloader(
self, config: Dict) -> 'torch.utils.data.DataLoader':
"""
Setup function for a temporary data loader which wraps the provided 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.joint.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
def on_after_backward(self):
super().on_after_backward()
if self._optim_variational_noise_std > 0 and self.global_step >= self._optim_variational_noise_start:
for param_name, param in self.decoder.named_parameters():
if param.grad is not None:
noise = torch.normal(
mean=0.0,
std=self._optim_variational_noise_std,
size=param.size(),
device=param.device,
dtype=param.dtype,
)
param.grad.data.add_(noise)
if self._optim_normalize_joint_txu:
T, U = self._optim_normalize_txu
if T is not None and U is not None:
for param_name, param in self.encoder.named_parameters():
if param.grad is not None:
param.grad.data.div_(U)
for param_name, param in self.decoder.named_parameters():
if param.grad is not None:
param.grad.data.div_(T)
if self._optim_normalize_encoder_norm:
for param_name, param in self.encoder.named_parameters():
if param.grad is not None:
norm = param.grad.norm()
param.grad.data.div_(norm)
if self._optim_normalize_decoder_norm:
for param_name, param in self.decoder.named_parameters():
if param.grad is not None:
norm = param.grad.norm()
param.grad.data.div_(norm)
if self._optim_normalize_joint_norm:
for param_name, param in self.joint.named_parameters():
if param.grad is not None:
norm = param.grad.norm()
param.grad.data.div_(norm)
class EncDecRNNTModel(ASRModel):
"""Base class for encoder decoder RNNT-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 = []
return result
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
# Required loss function
if not WARP_RNNT_AVAILABLE:
raise ImportError(
"Could not import `warprnnt_pytorch`.\n"
"Please visit https://github.com/HawkAaron/warp-transducer "
"and follow the steps in the readme to build and install the "
"pytorch bindings for RNNT Loss, or use the provided docker "
"container that supports RNN-T loss.")
# 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)
# Initialize components
self.preprocessor = EncDecRNNTModel.from_config_dict(
self.cfg.preprocessor)
self.encoder = EncDecRNNTModel.from_config_dict(self.cfg.encoder)
# Update config values required by components dynamically
with open_dict(self.cfg.decoder):
self.cfg.decoder.vocab_size = len(self.cfg.labels)
with open_dict(self.cfg.joint):
self.cfg.joint.num_classes = len(self.cfg.labels)
self.cfg.joint.vocabulary = self.cfg.labels
self.cfg.joint.jointnet.encoder_hidden = self.cfg.model_defaults.enc_hidden
self.cfg.joint.jointnet.pred_hidden = self.cfg.model_defaults.pred_hidden
self.decoder = EncDecRNNTModel.from_config_dict(self.cfg.decoder)
self.joint = EncDecRNNTModel.from_config_dict(self.cfg.joint)
self.loss = RNNTLoss(num_classes=self.joint.num_classes_with_blank - 1)
if hasattr(self.cfg,
'spec_augment') and self._cfg.spec_augment is not None:
self.spec_augmentation = EncDecRNNTModel.from_config_dict(
self.cfg.spec_augment)
else:
self.spec_augmentation = None
# Setup decoding objects
self.decoding = RNNTDecoding(
decoding_cfg=self.cfg.decoding,
decoder=self.decoder,
joint=self.joint,
vocabulary=self.joint.vocabulary,
)
# Setup WER calculation
self.wer = RNNTWER(decoding=self.decoding,
batch_dim_index=0,
use_cer=False,
log_prediction=True,
dist_sync_on_step=True)
# Whether to compute loss during evaluation
if 'compute_eval_loss' in self.cfg:
self.compute_eval_loss = self.cfg.compute_eval_loss
else:
self.compute_eval_loss = True
# Setup fused Joint step if flag is set
if self.joint.fuse_loss_wer:
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
@torch.no_grad()
def transcribe(self,
paths2audio_files: List[str],
batch_size: int = 4) -> 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.
Returns:
A list of transcriptions 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
try:
# 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:
encoded, encoded_len = self.forward(
input_signal=test_batch[0].to(device),
input_signal_length=test_batch[1].to(device))
hypotheses += self.decoding.rnnt_decoder_predictions_tensor(
encoded, encoded_len)
del test_batch
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
return hypotheses
def change_vocabulary(self,
new_vocabulary: List[str],
decoding_cfg: Optional[DictConfig] = None):
"""
Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning a pre-trained model.
This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would
use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need
model to learn capitalization, punctuation and/or special characters.
Args:
new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \
this is target alphabet.
decoding_cfg: A config for the decoder, which is optional. If the decoding type
needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here.
Returns: None
"""
if self.joint.vocabulary == new_vocabulary:
logging.warning(
f"Old {self.joint.vocabulary} and new {new_vocabulary} match. Not changing anything."
)
else:
if new_vocabulary is None or len(new_vocabulary) == 0:
raise ValueError(
f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}'
)
joint_config = self.joint.to_config_dict()
new_joint_config = copy.deepcopy(joint_config)
new_joint_config['vocabulary'] = new_vocabulary
new_joint_config['num_classes'] = len(new_vocabulary)
del self.joint
self.joint = EncDecRNNTModel.from_config_dict(new_joint_config)
decoder_config = self.decoder.to_config_dict()
new_decoder_config = copy.deepcopy(decoder_config)
new_decoder_config.vocab_size = len(new_vocabulary)
del self.decoder
self.decoder = EncDecRNNTModel.from_config_dict(new_decoder_config)
del self.loss
self.loss = RNNTLoss(
num_classes=self.joint.num_classes_with_blank - 1)
if decoding_cfg is None:
# Assume same decoding config as before
decoding_cfg = self.cfg.decoding
self.decoding = RNNTDecoding(
decoding_cfg=decoding_cfg,
decoder=self.decoder,
joint=self.joint,
vocabulary=self.joint.vocabulary,
)
self.wer = RNNTWER(
decoding=self.decoding,
batch_dim_index=self.wer.batch_dim_index,
use_cer=self.wer.use_cer,
log_prediction=self.wer.log_prediction,
dist_sync_on_step=True,
)
# Setup fused Joint step
if self.joint.fuse_loss_wer:
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
# Update config
with open_dict(self.cfg.joint):
self.cfg.joint = new_joint_config
with open_dict(self.cfg.decoder):
self.cfg.decoder = new_decoder_config
with open_dict(self.cfg.decoding):
self.cfg.decoding = decoding_cfg
logging.info(
f"Changed decoder to output to {self.joint.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', 'D', 'T'),
AcousticEncodedRepresentation()),
"encoded_lengths": NeuralType(tuple('B'), LengthsType()),
}
@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) is 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,
)
# 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)
encoded, encoded_len = self.encoder(audio_signal=processed_signal,
length=processed_signal_length)
return encoded, encoded_len
# PTL-specific methods
def training_step(self, batch, batch_nb):
signal, signal_len, transcript, transcript_len = batch
# forward() only performs encoder forward
if isinstance(batch, DALIOutputs) and batch.has_processed_signal:
encoded, encoded_len = self.forward(
processed_signal=signal, processed_signal_length=signal_len)
else:
encoded, encoded_len = self.forward(input_signal=signal,
input_signal_length=signal_len)
del signal
# During training, loss must be computed, so decoder forward is necessary
decoder, target_length = self.decoder(targets=transcript,
target_length=transcript_len)
if hasattr(self, '_trainer') and self._trainer is not None:
log_every_n_steps = self._trainer.log_every_n_steps
sample_id = self._trainer.global_step
else:
log_every_n_steps = 1
sample_id = batch_nb
# If experimental fused Joint-Loss-WER is not used
if not self.joint.fuse_loss_wer:
# Compute full joint and loss
joint = self.joint(encoder_outputs=encoded,
decoder_outputs=decoder)
loss_value = self.loss(log_probs=joint,
targets=transcript,
input_lengths=encoded_len,
target_lengths=target_length)
tensorboard_logs = {
'train_loss': loss_value,
'learning_rate': self._optimizer.param_groups[0]['lr']
}
if (sample_id + 1) % log_every_n_steps == 0:
self.wer.update(encoded, encoded_len, transcript,
transcript_len)
_, scores, words = self.wer.compute()
tensorboard_logs.update(
{'training_batch_wer': scores.float() / words})
else:
# If experimental fused Joint-Loss-WER is used
if (sample_id + 1) % log_every_n_steps == 0:
compute_wer = True
else:
compute_wer = False
# Fused joint step
loss_value, wer, _, _ = self.joint(
encoder_outputs=encoded,
decoder_outputs=decoder,
encoder_lengths=encoded_len,
transcripts=transcript,
transcript_lengths=transcript_len,
compute_wer=compute_wer,
)
tensorboard_logs = {
'train_loss': loss_value,
'learning_rate': self._optimizer.param_groups[0]['lr']
}
if compute_wer:
tensorboard_logs.update({'training_batch_wer': wer})
# Log items
self.log_dict(tensorboard_logs)
return {'loss': loss_value}
def validation_step(self, batch, batch_idx, dataloader_idx=0):
signal, signal_len, transcript, transcript_len = batch
# forward() only performs encoder forward
if isinstance(batch, DALIOutputs) and batch.has_processed_signal:
encoded, encoded_len = self.forward(
processed_signal=signal, processed_signal_length=signal_len)
else:
encoded, encoded_len = self.forward(input_signal=signal,
input_signal_length=signal_len)
del signal
tensorboard_logs = {}
# If experimental fused Joint-Loss-WER is not used
if not self.joint.fuse_loss_wer:
if self.compute_eval_loss:
decoder, target_length = self.decoder(
targets=transcript, target_length=transcript_len)
joint = self.joint(encoder_outputs=encoded,
decoder_outputs=decoder)
loss_value = self.loss(log_probs=joint,
targets=transcript,
input_lengths=encoded_len,
target_lengths=target_length)
tensorboard_logs['val_loss'] = loss_value
self.wer.update(encoded, encoded_len, transcript, transcript_len)
wer, wer_num, wer_denom = self.wer.compute()
tensorboard_logs['val_wer_num'] = wer_num
tensorboard_logs['val_wer_denom'] = wer_denom
tensorboard_logs['val_wer'] = wer
else:
# If experimental fused Joint-Loss-WER is used
compute_wer = True
if self.compute_eval_loss:
decoded, target_len = self.decoder(
targets=transcript, target_length=transcript_len)
else:
decoded = None
target_len = transcript_len
# Fused joint step
loss_value, wer, wer_num, wer_denom = self.joint(
encoder_outputs=encoded,
decoder_outputs=decoded,
encoder_lengths=encoded_len,
transcripts=transcript,
transcript_lengths=target_len,
compute_wer=compute_wer,
)
if loss_value is not None:
tensorboard_logs['val_loss'] = loss_value
tensorboard_logs['val_wer_num'] = wer_num
tensorboard_logs['val_wer_denom'] = wer_denom
tensorboard_logs['val_wer'] = wer
return tensorboard_logs
def test_step(self, batch, batch_idx, dataloader_idx=0):
logs = self.validation_step(batch,
batch_idx,
dataloader_idx=dataloader_idx)
test_logs = {
'test_wer_num': logs['val_wer_num'],
'test_wer_denom': logs['val_wer_denom'],
# 'test_wer': logs['val_wer'],
}
if 'val_loss' in logs:
test_logs['test_loss'] = logs['val_loss']
return test_logs
def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0):
if self.compute_eval_loss:
val_loss_mean = torch.stack([x['val_loss']
for x in outputs]).mean()
val_loss_log = {'val_loss': val_loss_mean}
else:
val_loss_log = {}
wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum()
wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum()
tensorboard_logs = {
**val_loss_log, 'val_wer': wer_num.float() / wer_denom
}
return {**val_loss_log, 'log': tensorboard_logs}
def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0):
if self.compute_eval_loss:
test_loss_mean = torch.stack([x['test_loss']
for x in outputs]).mean()
test_loss_log = {'test_loss': test_loss_mean}
else:
test_loss_log = {}
wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum()
wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum()
tensorboard_logs = {
**test_loss_log, 'test_wer': wer_num.float() / wer_denom
}
return {**test_loss_log, 'log': tensorboard_logs}
def _setup_transcribe_dataloader(
self, config: Dict) -> 'torch.utils.data.DataLoader':
"""
Setup function for a temporary data loader which wraps the provided 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.joint.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