def test_wer_metric_return_hypothesis(self, batch_dim_index, test_wer_bpe):
wer = WER(vocabulary=self.vocabulary.copy(),
batch_dim_index=batch_dim_index,
use_cer=False,
ctc_decode=True)
tensor = self.__string_to_ctc_tensor('cat', test_wer_bpe).int()
if batch_dim_index > 0:
tensor.transpose_(0, 1)
# pass batchsize 1 tensor, get back list of length 1 Hypothesis
hyp = wer.ctc_decoder_predictions_tensor(tensor,
return_hypotheses=True)
hyp = hyp[0]
assert isinstance(hyp, Hypothesis)
assert hyp.y_sequence is None
assert hyp.score == -1.0
assert hyp.text == 'cat'
assert hyp.alignments == [3, 1, 20]
assert hyp.length == 0
length = torch.tensor([tensor.shape[1 - batch_dim_index]],
dtype=torch.long)
# pass batchsize 1 tensor, get back list of length 1 Hypothesis [add length info]
hyp = wer.ctc_decoder_predictions_tensor(tensor,
predictions_len=length,
return_hypotheses=True)
hyp = hyp[0]
assert isinstance(hyp, Hypothesis)
assert hyp.length == 3
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 = 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
super().__init__(cfg=cfg, trainer=trainer)
self.preprocessor = EncDecCTCModel.from_config_dict(
self._cfg.preprocessor)
self.encoder = EncDecCTCModel.from_config_dict(self._cfg.encoder)
self.decoder = EncDecCTCModel.from_config_dict(self._cfg.decoder)
self.loss = CTCLoss(num_classes=self.decoder.num_classes_with_blank -
1,
zero_infinity=True)
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=False,
ctc_decode=True)
def test_wer_metric_randomized(self, test_wer_bpe):
"""This test relies on correctness of word_error_rate function."""
def __random_string(length):
return ''.join(
random.choice(''.join(self.vocabulary)) for _ in range(length))
if test_wer_bpe:
wer = WERBPE(deepcopy(self.char_tokenizer),
batch_dim_index=0,
use_cer=False,
ctc_decode=True)
else:
wer = WER(vocabulary=self.vocabulary,
batch_dim_index=0,
use_cer=False,
ctc_decode=True)
for test_id in range(256):
n1 = random.randint(1, 512)
n2 = random.randint(1, 512)
s1 = __random_string(n1)
s2 = __random_string(n2)
# skip empty strings as reference
if s2.strip():
assert (abs(
self.get_wer(wer,
prediction=s1,
reference=s2,
use_tokenizer=test_wer_bpe) -
word_error_rate(hypotheses=[s1], references=[s2])) < 1e-6)
def predict():
files = ['description.wav']
'''
for fname, transcription in zip(files, quartznet.transcribe(paths2audio_files=files)):
print(f"Audio in {fname} was recognized as: {transcription}")
'''
with tempfile.TemporaryDirectory() as tmpdir:
with open(os.path.join(tmpdir, 'manifest.json'), 'w') as fp:
for audio_file in files:
entry = {'audio_filepath': audio_file, 'duration': 100000, 'text': 'nothing'}
fp.write(json.dumps(entry) + '\n')
config = {'paths2audio_files': files, 'batch_size': 4, 'temp_dir': tmpdir}
temporary_datalayer = setup_transcribe_dataloader(config, quartznet.decoder.vocabulary)
for test_batch in temporary_datalayer:
processed_signal, processed_signal_len = quartznet.preprocessor(
input_signal=test_batch[0].to(quartznet.device), length=test_batch[1].to(quartznet.device)
)
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(processed_signal),}
ologits = ort_session.run(None, ort_inputs)
alogits = np.asarray(ologits)
logits = torch.from_numpy(alogits[0])
greedy_predictions = logits.argmax(dim=-1, keepdim=False)
wer = WER(vocabulary=quartznet.decoder.vocabulary, batch_dim_index=0, use_cer=False, ctc_decode=True)
hypotheses = wer.ctc_decoder_predictions_tensor(greedy_predictions)
print(hypotheses)
break
return jsonify({'prediccion' : hypotheses})
def inference(config_path: str, test_manifest: str, checkpoint: str = None):
"""
Inference for formated data
:param config_path:
:param test_manifest:
:param checkpoint:
:return:
"""
yaml = YAML(typ='safe')
with open(config_path) as f:
params = yaml.load(f)
params['model']['validation_ds']['manifest_filepath'] = test_manifest
# Bigger batch-size = bigger throughput
params['model']['validation_ds']['batch_size'] = 1
# Setup the test data loader and make sure the model is on GPU
# asr_model.restore_from(restore_path=str(WORK_DIR / 'checkpoint.nemo'))
asr_model = nemo_asr.models.EncDecCTCModel.load_from_checkpoint(
checkpoint_path=checkpoint)
asr_model.setup_test_data(
test_data_config=params['model']['validation_ds'])
asr_model.cuda()
wer = WER(vocabulary=asr_model.decoder.vocabulary)
for test_batch in asr_model.test_dataloader():
test_batch = [x.cuda() for x in test_batch]
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=test_batch[0], input_signal_length=test_batch[1])
pred = wer.ctc_decoder_predictions_tensor(greedy_predictions)
trans = wer.ctc_decoder_predictions_tensor(test_batch[2])
print("original: {}, prediction: {}".format(trans, pred))
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),
)
# Setup optional Optimization flags
self.setup_optimization_flags()
# Adapter modules setup (from ASRAdapterModelMixin)
self.setup_adapters()
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 "params" in self._cfg.decoder:
if "feat_in" not in self._cfg.decoder.params or (
not self._cfg.decoder.params.feat_in
and hasattr(self.encoder, '_feat_out')):
self._cfg.decoder.params.feat_in = self.encoder._feat_out
if "feat_in" not in self._cfg.decoder.params or not self._cfg.decoder.params.feat_in:
raise ValueError(
"param feat_in of the decoder's config is not set!")
else:
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),
)
def test_wer_metric_simple(self):
wer = WER(vocabulary=self.vocabulary, batch_dim_index=0, use_cer=False, ctc_decode=True)
assert self.get_wer(wer, 'cat', 'cot') == 1.0
assert self.get_wer(wer, 'gpu', 'g p u') == 1.0
assert self.get_wer(wer, 'g p u', 'gpu') == 3.0
assert self.get_wer(wer, 'ducati motorcycle', 'motorcycle') == 1.0
assert self.get_wer(wer, 'ducati motorcycle', 'ducuti motorcycle') == 0.5
assert self.get_wer(wer, 'a f c', 'a b c') == 1.0 / 3.0
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 test_wer_metric_decode(self):
wer = WER(vocabulary=self.vocabulary, batch_dim_index=0, use_cer=False, ctc_decode=True)
tokens = self.__string_to_ctc_tensor('cat')[0].int().numpy().tolist()
assert tokens == [3, 1, 20]
tokens_decoded = wer.decode_ids_to_tokens(tokens)
assert tokens_decoded == ['c', 'a', 't']
str_decoded = wer.decode_tokens_to_str(tokens)
assert str_decoded == 'cat'
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model", type=str, default="QuartzNet15x5Base-En", required=True, help="Pass: '******'",
)
parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--wer_tolerance", type=float, default=1.0, help="used by test")
parser.add_argument(
"--normalize_text", default=True, type=bool, help="Normalize transcripts or not. Set to False for non-English."
)
args = parser.parse_args()
torch.set_grad_enabled(False)
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': asr_model.decoder.vocabulary,
'batch_size': args.batch_size,
'normalize_transcripts': args.normalize_text,
}
)
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
labels_map = dict([(i, asr_model.decoder.vocabulary[i]) for i in range(len(asr_model.decoder.vocabulary))])
wer = WER(vocabulary=asr_model.decoder.vocabulary)
hypotheses = []
references = []
for test_batch in asr_model.test_dataloader():
if can_gpu:
test_batch = [x.cuda() for x in test_batch]
with autocast():
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=test_batch[0], input_signal_length=test_batch[1]
)
hypotheses += wer.ctc_decoder_predictions_tensor(greedy_predictions)
for batch_ind in range(greedy_predictions.shape[0]):
reference = ''.join([labels_map[c] for c in test_batch[2][batch_ind].cpu().detach().numpy()])
references.append(reference)
del test_batch
wer_value = word_error_rate(hypotheses=hypotheses, references=references)
if wer_value > args.wer_tolerance:
raise ValueError(f"Got WER of {wer_value}. It was higher than {args.wer_tolerance}")
logging.info(f'Got WER of {wer_value}. Tolerance was {args.wer_tolerance}')
def test_wer_metric_randomized(self):
"""This test relies on correctness of word_error_rate function."""
def __randomString(N):
return ''.join(random.choice(''.join(self.vocabulary)) for i in range(N))
wer = WER(vocabulary=self.vocabulary, batch_dim_index=0, use_cer=False, ctc_decode=True)
for test_id in range(256):
n1 = random.randint(1, 512)
n2 = random.randint(1, 512)
s1 = __randomString(n1)
s2 = __randomString(n2)
# Floating-point math doesn't seem to be an issue here. Leaving as ==
assert self.get_wer(wer, prediction=s1, reference=s2) == word_error_rate(hypotheses=[s1], references=[s2])
def generate_ref_hyps(asr_model: EncDecCTCModel, search: str, arpa: str):
if can_gpu:
asr_model = asr_model.cuda()
print("USING GPU!")
asr_model.eval()
vocabulary = asr_model.decoder.vocabulary
labels_map = dict([(i, vocabulary[i]) for i in range(len(vocabulary))])
wer = WER(vocabulary=vocabulary)
if search == "kenlm" or search == "beamsearch":
arpa_file = prepare_arpa_file(arpa)
lm_path = arpa_file if search == "kenlm" else None
beamsearcher = nemo_asr.modules.BeamSearchDecoderWithLM(
vocab=list(vocabulary),
beam_width=16,
alpha=2,
beta=1.5,
lm_path=lm_path,
num_cpus=max(os.cpu_count(), 1),
input_tensor=True,
)
for batch in asr_model.test_dataloader():
# TODO(tilo): test_loader should return dict or some typed object not tuple of tensors!!
if can_gpu:
batch = [x.cuda() for x in batch]
input_signal, inpsig_len, transcript, transc_len = batch
with autocast():
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=input_signal, input_signal_length=inpsig_len)
if search == "greedy":
decoded = wer.ctc_decoder_predictions_tensor(greedy_predictions)
else:
decoded = beamsearch_forward(beamsearcher,
log_probs=log_probs,
log_probs_length=encoded_len)
for i, hyp in enumerate(decoded):
reference = "".join([
labels_map[c]
for c in transcript[i].cpu().detach().numpy()[:transc_len[i]]
])
yield reference, hyp
def get_transcript(manifest_path: str,
asr_model: nemo_asr.models.EncDecCTCModel,
batch_size: int) -> List[str]:
"""
Returns transcripts for audio segments in the batch
Args:
manifest_path: path to the manifest for inference
asr_model: CTC-based ASR model, for example, QuartzNet15x5Base-En
batch_size: batch size
Returns: hypotheses: transcripts for the audio segments
"""
# batch inference
try:
from torch.cuda.amp import autocast
except ImportError:
from contextlib import contextmanager
@contextmanager
def autocast(enabled=None):
yield
torch.set_grad_enabled(False)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': manifest_path,
'labels': asr_model.decoder.vocabulary,
'batch_size': batch_size,
'normalize_transcripts': False,
})
asr_model.eval()
wer = WER(vocabulary=asr_model.decoder.vocabulary)
hypotheses = []
for test_batch in asr_model.test_dataloader():
if torch.cuda.is_available():
test_batch = [x.cuda() for x in test_batch]
with autocast():
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=test_batch[0], input_signal_length=test_batch[1])
hypotheses += wer.ctc_decoder_predictions_tensor(greedy_predictions)
del test_batch
torch.cuda.empty_cache()
return hypotheses
def test_wer_metric_decode(self, test_wer_bpe):
if test_wer_bpe:
wer = WERBPE(self.char_tokenizer,
batch_dim_index=0,
use_cer=False,
ctc_decode=True)
else:
wer = WER(vocabulary=self.vocabulary.copy(),
batch_dim_index=0,
use_cer=False,
ctc_decode=True)
tokens = self.__string_to_ctc_tensor(
'cat', use_tokenizer=test_wer_bpe)[0].int().numpy().tolist()
assert tokens == [3, 1, 20]
tokens_decoded = wer.decode_ids_to_tokens(tokens)
assert tokens_decoded == ['c', 'a', 't']
str_decoded = wer.decode_tokens_to_str(tokens)
assert str_decoded == 'cat'
def test_wer_metric_simple(self, batch_dim_index, test_wer_bpe):
if test_wer_bpe:
wer = WERBPE(self.char_tokenizer,
batch_dim_index,
use_cer=False,
ctc_decode=True)
else:
wer = WER(vocabulary=self.vocabulary,
batch_dim_index=batch_dim_index,
use_cer=False,
ctc_decode=True)
assert self.get_wer(wer, 'cat', 'cot', test_wer_bpe) == 1.0
assert self.get_wer(wer, 'gpu', 'g p u', test_wer_bpe) == 1.0
assert self.get_wer(wer, 'g p u', 'gpu', test_wer_bpe) == 3.0
assert self.get_wer(wer, 'ducati motorcycle', 'motorcycle',
test_wer_bpe) == 1.0
assert self.get_wer(wer, 'ducati motorcycle', 'ducuti motorcycle',
test_wer_bpe) == 0.5
assert abs(
self.get_wer(wer, 'a f c', 'a b c', test_wer_bpe) -
1.0 / 3.0) < 1e-6
def ASR_Grade(dataset, id, key):
try:
from torch.cuda.amp import autocast
except ImportError:
from contextlib import contextmanager
@contextmanager
def autocast(enabled=None):
yield
can_gpu = torch.cuda.is_available()
parser = ArgumentParser()
parser.add_argument(
"--asr_model",
type=str,
default=model_Selected,
required=True,
help=f'Pass: {model_Selected}',
)
parser.add_argument("--dataset",
type=str,
required=True,
help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--wer_tolerance",
type=float,
default=1.0,
help="used by test")
parser.add_argument(
"--normalize_text",
default=False, # False <- we're using phonetic references
type=bool,
help="Normalize transcripts or not. Set to False for non-English.",
)
args = parser.parse_args(
["--dataset", dataset, "--asr_model", model_Selected])
torch.set_grad_enabled(False)
# Instantiate Jasper/QuartzNet models with the EncDecCTCModel class.
asr_model = EncDecCTCModel.restore_from(model_Path)
asr_model.setup_test_data(
test_data_config={
"sample_rate": 16000,
"manifest_filepath": args.dataset,
"labels": asr_model.decoder.vocabulary,
"batch_size": args.batch_size,
"normalize_transcripts": args.normalize_text,
})
if can_gpu: # noqa
asr_model = asr_model.cuda()
asr_model.eval()
labels_map = dict([(i, asr_model.decoder.vocabulary[i])
for i in range(len(asr_model.decoder.vocabulary))])
wer = WER(vocabulary=asr_model.decoder.vocabulary)
hypotheses = []
references = []
for test_batch in asr_model.test_dataloader():
if can_gpu:
test_batch = [x.cuda() for x in test_batch]
with autocast():
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=test_batch[0], input_signal_length=test_batch[1])
hypotheses = wer.ctc_decoder_predictions_tensor(greedy_predictions)
for batch_ind in range(greedy_predictions.shape[0]):
reference = key
#reference = "".join([labels_map[c] for c in test_batch[2][batch_ind].cpu().detach().numpy()]) #debug
print(reference) #debug
references.append(reference)
del test_batch
wer_value = word_error_rate(hypotheses=hypotheses,
references=references) #cer=True
REC = '.'
REF = '.'
for h, r in zip(hypotheses, references):
print("Recognized:\t{}\nReference:\t{}\n".format(h, r))
REC = h
REF = r
logging.info(f"Got PER of {wer_value}. Tolerance was {args.wer_tolerance}")
#Score Calculation, phoneme conversion
# divide wer_value by wer_tolerance to get the ratio of correctness (and round it)
# then multiply by 100 to get a value above 0
# since this give the "% wrong", subtract from 100 to get "% correct"
# this gives a positive grade to show return to the user
score = 100.00 - (round((wer_value / args.wer_tolerance), 4) * 100)
if score < 0.0:
score = 0.0
print(score)
#Result file creation, to be accessed by JS via 'app.py'
Results = open(datasetPath + id + '_graded.txt', 'w')
Results.write(REC + '\n' + REF + '\n' + str(score))
Results.close()
return score
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model",
type=str,
default="QuartzNet15x5Base-En",
required=True,
help="Pass: '******'",
)
parser.add_argument("--dataset",
type=str,
required=True,
help="path to evaluation data")
parser.add_argument("--wer_target",
type=float,
default=None,
help="used by test")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--wer_tolerance",
type=float,
default=1.0,
help="used by test")
parser.add_argument(
"--dont_normalize_text",
default=False,
action='store_false',
help="Turn off trasnscript normalization. Recommended for non-English.",
)
parser.add_argument(
"--use_cer",
default=False,
action='store_true',
help="Use Character Error Rate as the evaluation metric")
parser.add_argument('--sensitivity',
action="store_true",
help="Perform sensitivity analysis")
parser.add_argument('--onnx', action="store_true", help="Export to ONNX")
parser.add_argument('--quant-disable-keyword',
type=str,
nargs='+',
help='disable quantizers by keyword')
args = parser.parse_args()
torch.set_grad_enabled(False)
quant_modules.initialize()
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model_cfg = EncDecCTCModelBPE.restore_from(
restore_path=args.asr_model, return_config=True)
with open_dict(asr_model_cfg):
asr_model_cfg.encoder.quantize = True
asr_model = EncDecCTCModelBPE.restore_from(
restore_path=args.asr_model, override_config_path=asr_model_cfg)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model_cfg = EncDecCTCModelBPE.from_pretrained(
model_name=args.asr_model, return_config=True)
with open_dict(asr_model_cfg):
asr_model_cfg.encoder.quantize = True
asr_model = EncDecCTCModelBPE.from_pretrained(
model_name=args.asr_model, override_config_path=asr_model_cfg)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': asr_model.decoder.vocabulary,
'batch_size': args.batch_size,
'normalize_transcripts': args.dont_normalize_text,
})
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
if args.quant_disable_keyword:
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
for keyword in args.quant_disable_keyword:
if keyword in name:
logging.warning(F"Disable {name}")
module.disable()
labels_map = dict([(i, asr_model.decoder.vocabulary[i])
for i in range(len(asr_model.decoder.vocabulary))])
wer = WER(vocabulary=asr_model.decoder.vocabulary, use_cer=args.use_cer)
wer_quant = evaluate(asr_model, labels_map, wer)
logging.info(f'Got WER of {wer_quant}. Tolerance was {args.wer_tolerance}')
if args.sensitivity:
if wer_quant < args.wer_tolerance:
logging.info(
"Tolerance is already met. Skip sensitivity analyasis.")
return
quant_layer_names = []
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
module.disable()
layer_name = name.replace("._input_quantizer",
"").replace("._weight_quantizer", "")
if layer_name not in quant_layer_names:
quant_layer_names.append(layer_name)
logging.info(F"{len(quant_layer_names)} quantized layers found.")
# Build sensitivity profile
quant_layer_sensitivity = {}
for i, quant_layer in enumerate(quant_layer_names):
logging.info(F"Enable {quant_layer}")
for name, module in asr_model.named_modules():
if isinstance(
module,
quant_nn.TensorQuantizer) and quant_layer in name:
module.enable()
logging.info(F"{name:40}: {module}")
# Eval the model
wer_value = evaluate(asr_model, labels_map, wer)
logging.info(F"WER: {wer_value}")
quant_layer_sensitivity[
quant_layer] = args.wer_tolerance - wer_value
for name, module in asr_model.named_modules():
if isinstance(
module,
quant_nn.TensorQuantizer) and quant_layer in name:
module.disable()
logging.info(F"{name:40}: {module}")
# Skip most sensitive layers until WER target is met
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
module.enable()
quant_layer_sensitivity = collections.OrderedDict(
sorted(quant_layer_sensitivity.items(), key=lambda x: x[1]))
pprint(quant_layer_sensitivity)
skipped_layers = []
for quant_layer, _ in quant_layer_sensitivity.items():
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if quant_layer in name:
logging.info(F"Disable {name}")
if not quant_layer in skipped_layers:
skipped_layers.append(quant_layer)
module.disable()
wer_value = evaluate(asr_model, labels_map, wer)
if wer_value <= args.wer_tolerance:
logging.info(
F"WER tolerance {args.wer_tolerance} is met by skipping {len(skipped_layers)} sensitive layers."
)
print(skipped_layers)
export_onnx(args, asr_model)
return
raise ValueError(
f"WER tolerance {args.wer_tolerance} can not be met with any layer quantized!"
)
export_onnx(args, asr_model)
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model",
type=str,
default="QuartzNet15x5Base-En",
required=False,
help="Pass: '******'",
)
parser.add_argument("--dataset",
type=str,
required=True,
help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument(
"--normalize_text",
default=True,
type=bool,
help="Normalize transcripts or not. Set to False for non-English.")
parser.add_argument(
"--sclite_fmt",
default="trn",
type=str,
help="sclite output format. Only trn and ctm are supported")
parser.add_argument("--out_dir",
type=str,
required=True,
help="Destination dir for output files")
parser.add_argument("--sctk_dir",
type=str,
required=False,
default="",
help="Path to sctk root dir")
parser.add_argument("--glm",
type=str,
required=False,
default="",
help="Path to glm file")
parser.add_argument("--ref_stm",
type=str,
required=False,
default="",
help="Path to glm file")
args = parser.parse_args()
torch.set_grad_enabled(False)
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
use_sctk = os.path.exists(args.sctk_dir)
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': asr_model.decoder.vocabulary,
'batch_size': args.batch_size,
'normalize_transcripts': args.normalize_text,
})
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
labels_map = dict([(i, asr_model.decoder.vocabulary[i])
for i in range(len(asr_model.decoder.vocabulary))])
wer = WER(vocabulary=asr_model.decoder.vocabulary)
hypotheses = []
references = []
all_log_probs = []
for test_batch in asr_model.test_dataloader():
if can_gpu:
test_batch = [x.cuda() for x in test_batch]
with autocast():
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=test_batch[0], input_signal_length=test_batch[1])
for r in log_probs.cpu().numpy():
all_log_probs.append(r)
hypotheses += wer.ctc_decoder_predictions_tensor(greedy_predictions)
for batch_ind in range(greedy_predictions.shape[0]):
reference = ''.join([
labels_map[c]
for c in test_batch[2][batch_ind].cpu().detach().numpy()
])
references.append(reference)
del test_batch
info_list = get_utt_info(args.dataset)
hypfile = os.path.join(args.out_dir, "hyp.trn")
reffile = os.path.join(args.out_dir, "ref.trn")
with open(hypfile, "w") as hyp_f, open(reffile, "w") as ref_f:
for i in range(len(hypotheses)):
utt_id = os.path.splitext(
os.path.basename(info_list[i]['audio_filepath']))[0]
# rfilter in sctk likes each transcript to have a space at the beginning
hyp_f.write(" " + hypotheses[i] + " (" + utt_id + ")" + "\n")
ref_f.write(" " + references[i] + " (" + utt_id + ")" + "\n")
if use_sctk:
score_with_sctk(args.sctk_dir,
reffile,
hypfile,
args.out_dir,
glm=args.glm,
fmt="trn")
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model", type=str, default="QuartzNet15x5Base-En", required=True, help="Pass: '******'",
)
parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data")
parser.add_argument("--wer_target", type=float, default=None, help="used by test")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--wer_tolerance", type=float, default=1.0, help="used by test")
parser.add_argument(
"--normalize_text", default=True, type=bool, help="Normalize transcripts or not. Set to False for non-English."
)
parser.add_argument('--sensitivity', action="store_true", help="Perform sensitivity analysis")
parser.add_argument('--onnx', action="store_true", help="Export to ONNX")
args = parser.parse_args()
torch.set_grad_enabled(False)
quant_modules.initialize()
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': asr_model.decoder.vocabulary,
'batch_size': args.batch_size,
'normalize_transcripts': args.normalize_text,
}
)
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
labels_map = dict([(i, asr_model.decoder.vocabulary[i]) for i in range(len(asr_model.decoder.vocabulary))])
wer = WER(vocabulary=asr_model.decoder.vocabulary)
wer_quant = evaluate(asr_model, labels_map, wer)
logging.info(f'Got WER of {wer_quant}. Tolerance was {args.wer_tolerance}')
if args.sensitivity:
if wer_quant < args.wer_tolerance:
logging.info("Tolerance is already met. Skip sensitivity analyasis.")
return
quant_layer_names = []
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
module.disable()
layer_name = name.replace("._input_quantizer", "").replace("._weight_quantizer", "")
if layer_name not in quant_layer_names:
quant_layer_names.append(layer_name)
logging.info(F"{len(quant_layer_names)} quantized layers found.")
# Build sensitivity profile
quant_layer_sensitivity = {}
for i, quant_layer in enumerate(quant_layer_names):
logging.info(F"Enable {quant_layer}")
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer) and quant_layer in name:
module.enable()
logging.info(F"{name:40}: {module}")
# Eval the model
wer_value = evaluate(asr_model, labels_map, wer)
logging.info(F"WER: {wer_value}")
quant_layer_sensitivity[quant_layer] = args.wer_tolerance - wer_value
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer) and quant_layer in name:
module.disable()
logging.info(F"{name:40}: {module}")
# Skip most sensitive layers until WER target is met
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
module.enable()
quant_layer_sensitivity = collections.OrderedDict(sorted(quant_layer_sensitivity.items(), key=lambda x: x[1]))
pprint(quant_layer_sensitivity)
skipped_layers = []
for quant_layer, _ in quant_layer_sensitivity.items():
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if quant_layer in name:
logging.info(F"Disable {name}")
if not quant_layer in skipped_layers:
skipped_layers.append(quant_layer)
module.disable()
wer_value = evaluate(asr_model, labels_map, wer)
if wer_value <= args.wer_tolerance:
logging.info(
F"WER tolerance {args.wer_tolerance} is met by skipping {len(skipped_layers)} sensitive layers."
)
print(skipped_layers)
return
raise ValueError(f"WER tolerance {args.wer_tolerance} can not be met with any layer quantized!")
if args.onnx:
if args.asr_model.endswith("nemo"):
onnx_name = args.asr_model.replace(".nemo", ".onnx")
else:
onnx_name = args.asr_model
logging.info("Export to ", onnx_name)
quant_nn.TensorQuantizer.use_fb_fake_quant = True
asr_model.export(onnx_name, onnx_opset_version=13)
quant_nn.TensorQuantizer.use_fb_fake_quant = False
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model",
type=str,
default="QuartzNet15x5Base-En",
required=True,
help="Pass: '******'",
)
parser.add_argument(
"--asr_onnx",
type=str,
default="./QuartzNet15x5Base-En-max-32.onnx",
help="Pass: '******'",
)
parser.add_argument("--dataset",
type=str,
required=True,
help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument(
"--dont_normalize_text",
default=False,
action='store_false',
help="Turn off trasnscript normalization. Recommended for non-English.",
)
parser.add_argument(
"--use_cer",
default=False,
action='store_true',
help="Use Character Error Rate as the evaluation metric")
parser.add_argument('--qat',
action="store_true",
help="Use onnx file exported from QAT tools")
args = parser.parse_args()
torch.set_grad_enabled(False)
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model_cfg = EncDecCTCModel.restore_from(
restore_path=args.asr_model, return_config=True)
with open_dict(asr_model_cfg):
asr_model_cfg.encoder.quantize = True
asr_model = EncDecCTCModel.restore_from(
restore_path=args.asr_model, override_config_path=asr_model_cfg)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model_cfg = EncDecCTCModel.from_pretrained(
model_name=args.asr_model, return_config=True)
with open_dict(asr_model_cfg):
asr_model_cfg.encoder.quantize = True
asr_model = EncDecCTCModel.from_pretrained(
model_name=args.asr_model, override_config_path=asr_model_cfg)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': asr_model.decoder.vocabulary,
'batch_size': args.batch_size,
'normalize_transcripts': args.dont_normalize_text,
})
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
labels_map = dict([(i, asr_model.decoder.vocabulary[i])
for i in range(len(asr_model.decoder.vocabulary))])
wer = WER(vocabulary=asr_model.decoder.vocabulary, use_cer=args.use_cer)
wer_result = evaluate(asr_model, args.asr_onnx, labels_map, wer, args.qat)
logging.info(f'Got WER of {wer_result}.')
def main():
parser = ArgumentParser()
"""Training arguments"""
parser.add_argument("--asr_model",
type=str,
default="QuartzNet15x5Base-En",
required=True,
help="Pass: '******'")
parser.add_argument("--dataset",
type=str,
required=True,
help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument(
"--normalize_text",
default=True,
type=bool,
help="Normalize transcripts or not. Set to False for non-English.")
parser.add_argument("--shuffle",
action='store_true',
help="Shuffle test data.")
"""Calibration arguments"""
parser.add_argument("--load",
type=str,
default=None,
help="load path for the synthetic data")
parser.add_argument(
"--percentile",
type=float,
default=None,
help="Max/min percentile for outlier handling. e.g., 99.9")
"""Quantization arguments"""
parser.add_argument("--weight_bit",
type=int,
default=8,
help="quantization bit for weights")
parser.add_argument("--act_bit",
type=int,
default=8,
help="quantization bit for activations")
parser.add_argument("--dynamic",
action='store_true',
help="Dynamic quantization mode.")
parser.add_argument("--no_quant",
action='store_true',
help="No quantization mode.")
"""Debugging arguments"""
parser.add_argument("--eval_early_stop",
type=int,
default=None,
help="early stop for debugging")
parser.add_argument("--calib_early_stop",
type=int,
default=None,
help="early stop calibration")
args = parser.parse_args()
torch.set_grad_enabled(False)
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': asr_model.decoder.vocabulary,
'batch_size': args.batch_size,
'normalize_transcripts': args.normalize_text,
'shuffle': args.shuffle,
})
if args.load is not None:
print('Data loaded from %s' % args.load)
with open(args.load, 'rb') as f:
distilled_data = pickle.load(f)
synthetic_batch_size, _, synthetic_seqlen = distilled_data[0].shape
else:
assert args.dynamic, \
"synthetic data must be loaded unless running with the dynamic quantization mode"
############################## Calibration #####################################
torch.set_grad_enabled(False) # disable backward graph generation
asr_model.eval() # evaluation mode
asr_model.set_quant_bit(args.weight_bit, mode='weight')
asr_model.set_quant_bit(args.act_bit, mode='act')
# set percentile
if args.percentile is not None:
qm.set_percentile(asr_model, args.percentile)
if args.no_quant:
asr_model.set_quant_mode('none')
else:
asr_model.encoder.bn_folding() # BN folding
# if not dynamic quantization, calibrate min/max/range for the activations using synthetic data
# if dynamic, we can skip calibration
if not args.dynamic:
print('Calibrating...')
qm.calibrate(asr_model)
length = torch.tensor([synthetic_seqlen] * synthetic_batch_size).cuda()
for batch_idx, inputs in enumerate(distilled_data):
if args.calib_early_stop is not None and batch_idx == args.calib_early_stop:
break
inputs = inputs.cuda()
encoded, encoded_len, encoded_scaling_factor = asr_model.encoder(
audio_signal=inputs, length=length)
log_probs = asr_model.decoder(
encoder_output=encoded,
encoder_output_scaling_factor=encoded_scaling_factor)
############################## Evaluation #####################################
print('Evaluating...')
qm.evaluate(asr_model)
qm.set_dynamic(
asr_model,
args.dynamic) # if dynamic quantization, this will be enabled
labels_map = dict([(i, asr_model.decoder.vocabulary[i])
for i in range(len(asr_model.decoder.vocabulary))])
wer = WER(vocabulary=asr_model.decoder.vocabulary)
hypotheses = []
references = []
progress_bar = tqdm(asr_model.test_dataloader())
for i, test_batch in enumerate(progress_bar):
if i == args.eval_early_stop:
break
test_batch = [x.cuda().float() for x in test_batch]
with autocast():
log_probs, encoded_len, greedy_predictions = asr_model(
input_signal=test_batch[0], input_signal_length=test_batch[1])
hypotheses += wer.ctc_decoder_predictions_tensor(greedy_predictions)
for batch_ind in range(greedy_predictions.shape[0]):
reference = ''.join([
labels_map[c]
for c in test_batch[2][batch_ind].cpu().detach().numpy()
])
references.append(reference)
del test_batch
wer_value = word_error_rate(hypotheses=hypotheses, references=references)
print('WER:', wer_value)