def classifytext(self, queries: List[str], batch_size: int = 1, prompt: str = 'Sentiment') -> List[int]:
"""
Get prediction for the queries
Args:
queries: text sequences
batch_size: batch size to use during inference
prompt: the prompt string appended at the end of your input sentence
Returns:
all_preds: model predictions
"""
# store predictions for all queries in a single list
all_preds = []
mode = self.training
try:
# Switch model to evaluation mode
self.eval()
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
dataloader_cfg = {"batch_size": batch_size, "num_workers": 3, "pin_memory": False}
infer_datalayer = self._setup_infer_dataloader(dataloader_cfg, queries, prompt)
for i, batch in enumerate(infer_datalayer):
sentences, _ = batch
preds = self.forward_eval(sentences)
all_preds.extend([self.id_to_label[i.item()] for i in preds])
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
return all_preds
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 test_SpectrogramAugmentationr_numba_kernel(self, caplog):
numba_utils.skip_numba_cuda_test_if_unsupported(__NUMBA_MINIMUM_VERSION__)
logging._logger.propagate = True
original_verbosity = logging.get_verbosity()
logging.set_verbosity(logging.DEBUG)
caplog.set_level(logging.DEBUG)
# Make sure constructor works
instance1 = modules.SpectrogramAugmentation(
freq_masks=10, time_masks=3, rect_masks=3, use_numba_spec_augment=True
)
assert isinstance(instance1, modules.SpectrogramAugmentation)
# Make sure forward doesn't throw with expected input
instance0 = modules.AudioToMelSpectrogramPreprocessor(dither=0)
input_signal = torch.randn(size=(8, 512))
length = torch.randint(low=161, high=500, size=[8])
res0 = instance0(input_signal=input_signal, length=length)
res = instance1(input_spec=res0[0], length=length)
assert res.shape == res0[0].shape
# check tha numba kernel debug message indicates that it is available for use
assert """Numba SpecAugment kernel is available""" in caplog.text
logging._logger.propagate = False
logging.set_verbosity(original_verbosity)
def ptune_inference(self,
queries: List[Dict],
batch_size: int = 1,
decode_token_len: int = 5) -> List[str]:
"""
Get prediction for the queries
Args:
queries: List of data samples without labels
batch_size: batch size to use during inference
decode_token_len: max number of tokens to generate during inference
Returns:
all_preds: model predictions
"""
# store predictions for all queries in a single list
all_preds = []
mode = self.training
try:
# Switch model to evaluation mode
self.eval()
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
dataloader_cfg = {
"batch_size": batch_size,
"num_workers": 3,
"pin_memory": False
}
infer_datalayer = self._setup_infer_dataloader(
dataloader_cfg, queries, decode_token_len)
for i, batch in enumerate(infer_datalayer):
enc_query = batch['enc_query'].to(self.device)
label_position = batch['label_position'].to(self.device)
enc_taskname = batch['enc_taskname'].to(self.device)
# loss, tokens_enc, labels, enc_mask, encoder_input = self.get_loss(batch)
predicted_token_ids, _ = self.decode(
enc_query=enc_query,
enc_taskname=enc_taskname,
label_position=label_position,
num_tokens_to_generate=self.num_tokens_to_gen,
)
preds = predicted_token_ids.cpu().numpy().tolist()
label_positions = label_position.cpu().numpy().tolist()
for i, (pred, label_position) in enumerate(
zip(preds, label_positions)):
start_position = label_position[0] + 1
pred = pred[start_position:]
if self.tokenizer.eos_id in pred:
idx = pred.index(self.tokenizer.eos_id)
pred = pred[:idx]
pred = [id for id in pred if id not in self.special_tokens]
pred = self.tokenizer.ids_to_text(pred)
all_preds.append(pred)
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
return all_preds
def classifytext(self,
queries: List[str],
batch_size: int = 1,
max_seq_length: int = -1) -> List[int]:
"""
Get prediction for the queries
Args:
queries: text sequences
batch_size: batch size to use during inference
max_seq_length: sequences longer than max_seq_length will get truncated. default -1 disables truncation.
Returns:
all_preds: model predictions
"""
# store predictions for all queries in a single list
all_preds = []
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)
dataloader_cfg = {
"batch_size": batch_size,
"num_workers": 3,
"pin_memory": False
}
infer_datalayer = self._setup_infer_dataloader(
dataloader_cfg, queries, max_seq_length)
for i, batch in enumerate(infer_datalayer):
input_ids, input_type_ids, input_mask, subtokens_mask = batch
logits = self.forward(
input_ids=input_ids.to(device),
token_type_ids=input_type_ids.to(device),
attention_mask=input_mask.to(device),
)
preds = tensor2list(torch.argmax(logits, axis=-1))
all_preds.extend(preds)
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
return all_preds
def test_base_model_no_support_for_adapters(self, caplog):
logging._logger.propagate = True
original_verbosity = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
caplog.set_level(logging.WARNING)
cfg = get_model_config(in_features=50, update_adapter_cfg=False)
model = DefaultAdapterModel(cfg)
with pytest.raises(AttributeError):
model.add_adapter(name='adapter_0', cfg=get_adapter_cfg())
# check that warning message indicates that it module is not available
assert """Encoder does not support adapters !""" in caplog.text
caplog.clear()
model.get_enabled_adapters()
# check that there is not warning message, since it should log only once.
assert """Encoder does not support adapters !""" not in caplog.text
logging._logger.propagate = False
logging.set_verbosity(original_verbosity)
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 transcribe(self,
paths2audio_files: List[str],
batch_size: int = 4,
logprobs=False) -> List[str]:
"""
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.
logprobs: (bool) pass True to get log probabilities instead of class labels.
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
labels = []
# 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:
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': paths2audio_files,
'batch_size': batch_size,
'temp_dir': tmpdir
}
temporary_datalayer = self._setup_transcribe_dataloader(config)
for test_batch in temporary_datalayer:
logits = 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]
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.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
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 labels
def inference(
self,
file: str,
batch_size: int = 1,
num_samples: int = -1,
output_nbest_file: Optional[str] = None,
output_prediction_file: Optional[str] = None,
):
"""
Get prediction for unlabeled inference data
Args:
file: inference data
batch_size: batch size to use during inference
num_samples: number of samples to use of inference data. Default: -1 if all data should be used.
output_nbest_file: optional output file for writing out nbest list
output_prediction_file: optional output file for writing out predictions
Returns:
model predictions, model nbest list
"""
# store predictions for all queries in a single list
all_predictions = []
all_nbest = []
mode = self.training
device = 'cuda' if torch.cuda.is_available() else 'cpu'
try:
# Switch model to evaluation mode
self.eval()
self.to(device)
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
dataloader_cfg = {
"batch_size": batch_size,
"file": file,
"shuffle": False,
"num_samples": num_samples,
'num_workers': 2,
'pin_memory': False,
'drop_last': False,
}
dataloader_cfg = OmegaConf.create(dataloader_cfg)
infer_datalayer = self._setup_dataloader_from_config(
cfg=dataloader_cfg, mode=INFERENCE_MODE)
all_logits = []
all_unique_ids = []
for i, batch in enumerate(infer_datalayer):
input_ids, token_type_ids, attention_mask, unique_ids = batch
logits = self.forward(
input_ids=input_ids.to(device),
token_type_ids=token_type_ids.to(device),
attention_mask=attention_mask.to(device),
)
all_logits.append(logits)
all_unique_ids.append(unique_ids)
logits = torch.cat(all_logits)
unique_ids = tensor2list(torch.cat(all_unique_ids))
s, e = logits.split(dim=-1, split_size=1)
start_logits = tensor2list(s.squeeze(-1))
end_logits = tensor2list(e.squeeze(-1))
(all_predictions, all_nbest,
scores_diff) = infer_datalayer.dataset.get_predictions(
unique_ids=unique_ids,
start_logits=start_logits,
end_logits=end_logits,
n_best_size=self._cfg.dataset.n_best_size,
max_answer_length=self._cfg.dataset.max_answer_length,
version_2_with_negative=self._cfg.dataset.
version_2_with_negative,
null_score_diff_threshold=self._cfg.dataset.
null_score_diff_threshold,
do_lower_case=self._cfg.dataset.do_lower_case,
)
with open(file, 'r') as test_file_fp:
test_data = json.load(test_file_fp)["data"]
id_to_question_mapping = {}
for title in test_data:
for par in title["paragraphs"]:
for question in par["qas"]:
id_to_question_mapping[
question["id"]] = question["question"]
for question_id in all_predictions:
all_predictions[question_id] = (
id_to_question_mapping[question_id],
all_predictions[question_id])
if output_nbest_file is not None:
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest, indent=4) + "\n")
if output_prediction_file is not None:
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
return all_predictions, all_nbest
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 transcribe(
self,
paths2audio_files: List[str],
batch_size: int = 4,
return_hypotheses: bool = False,
partial_hypothesis: Optional[List['Hypothesis']] = None,
num_workers: int = 0,
) -> (List[str], Optional[List['Hypothesis']]):
"""
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
num_workers: (int) number of workers for DataLoader
Returns:
A list of transcriptions in the same order as paths2audio_files. Will also return
"""
if paths2audio_files is None or len(paths2audio_files) == 0:
return {}
# We will store transcriptions here
hypotheses = []
all_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
if num_workers is None:
num_workers = min(batch_size, os.cpu_count() - 1)
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()
self.joint.freeze()
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
# Work in tmp directory - will store manifest file there
with tempfile.TemporaryDirectory() as tmpdir:
with open(os.path.join(tmpdir, 'manifest.json'), 'w', encoding='utf-8') as fp:
for 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"):
encoded, encoded_len = self.forward(
input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device)
)
best_hyp, all_hyp = self.decoding.rnnt_decoder_predictions_tensor(
encoded,
encoded_len,
return_hypotheses=return_hypotheses,
partial_hypotheses=partial_hypothesis,
)
hypotheses += best_hyp
if all_hyp is not None:
all_hypotheses += all_hyp
else:
all_hypotheses += best_hyp
del encoded
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)
if mode is True:
self.encoder.unfreeze()
self.decoder.unfreeze()
self.joint.unfreeze()
return hypotheses, all_hypotheses
def ptune_inference(self,
queries: List[Dict],
batch_size: int = 1,
decode_token_len: int = None) -> List[str]:
"""
Get prediction for the queries
Args:
queries: List of data samples without labels
batch_size: batch size to use during inference
decode_token_len: max number of tokens to generate during inference
Returns:
all_preds: model predictions
"""
if decode_token_len is None:
decode_token_len = self.decoder_seq_length
# store predictions for all queries in a single list
all_preds = []
mode = self.training
try:
# Switch model to evaluation mode
self.eval()
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
dataloader_cfg = {
"batch_size": batch_size,
"num_workers": 3,
"pin_memory": False
}
infer_datalayer = self._setup_infer_dataloader(
dataloader_cfg, queries, decode_token_len)
for i, batch in enumerate(infer_datalayer):
tokens_enc = batch['text_enc'].to(self.device)
enc_taskname = batch['enc_taskname'].to(self.device)
enc_mask = batch['enc_mask'].to(self.device)
input_embeds = self.embed_input(tokens_enc, enc_taskname)
encoder_position_ids = build_position_ids(tokens_enc)
position_embeddings = self.position_embeddings(
encoder_position_ids)
encoder_input = input_embeds + position_embeddings
# loss, tokens_enc, labels, enc_mask, encoder_input = self.get_loss(batch)
if self.float_type == torch.float32:
predicted_token_ids, _ = self.model.decode(
tokens_enc=tokens_enc,
enc_mask=enc_mask,
num_tokens_to_generate=decode_token_len,
enc_input=encoder_input,
)
else:
with torch.autocast(device_type="cuda",
dtype=self.float_type):
predicted_token_ids, _ = self.model.decode(
tokens_enc=tokens_enc,
enc_mask=enc_mask,
num_tokens_to_generate=decode_token_len,
enc_input=encoder_input,
)
preds = predicted_token_ids.cpu().numpy().tolist()
for i, pred in enumerate(preds):
if self.tokenizer.eos_id in pred:
idx = pred.index(self.tokenizer.eos_id)
pred = pred[:idx]
pred = [
id for id in pred if id not in
self.tokenizer.special_token_to_id.values()
]
pred = self.tokenizer.ids_to_text(pred)
all_preds.append(pred)
finally:
# set mode back to its original value
self.train(mode=mode)
logging.set_verbosity(logging_level)
return all_preds
def transcribe_partial_audio(
asr_model,
path2manifest: str,
batch_size: int = 4,
logprobs: bool = False,
return_hypotheses: bool = False,
num_workers: int = 0,
) -> List[str]:
assert isinstance(asr_model,
EncDecCTCModel), "Currently support CTC model only."
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 = asr_model.training
device = next(asr_model.parameters()).device
dither_value = asr_model.preprocessor.featurizer.dither
pad_to_value = asr_model.preprocessor.featurizer.pad_to
try:
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
# Switch model to evaluation mode
asr_model.eval()
# Freeze the encoder and decoder modules
asr_model.encoder.freeze()
asr_model.decoder.freeze()
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
config = {
'manifest_filepath': path2manifest,
'batch_size': batch_size,
'num_workers': num_workers,
}
temporary_datalayer = asr_model._setup_transcribe_dataloader(config)
for test_batch in tqdm(temporary_datalayer, desc="Transcribing"):
logits, logits_len, greedy_predictions = asr_model.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 = asr_model._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
asr_model.train(mode=mode)
asr_model.preprocessor.featurizer.dither = dither_value
asr_model.preprocessor.featurizer.pad_to = pad_to_value
if mode is True:
asr_model.encoder.unfreeze()
asr_model.decoder.unfreeze()
logging.set_verbosity(logging_level)
return hypotheses
