def test_EncDecCTCModel(self):
# TODO: Switch to using named configs because here we don't really care about weights
qn = EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
self.__test_restore_elsewhere(
model=qn,
attr_for_eq_check=set(["decoder._feat_in",
"decoder._num_classes"]))
def set_asr_model(self, asr_model):
"""
Setup the parameters for the given ASR model
Currently, the following models are supported:
stt_en_conformer_ctc_large
stt_en_conformer_ctc_medium
stt_en_conformer_ctc_small
QuartzNet15x5Base-En
"""
if 'QuartzNet' in asr_model:
self.run_ASR = self.run_ASR_QuartzNet_CTC
asr_model = EncDecCTCModel.from_pretrained(model_name=asr_model,
strict=False)
self.params['offset'] = -0.18
self.model_stride_in_secs = 0.02
self.asr_delay_sec = -1 * self.params['offset']
elif 'conformer_ctc' in asr_model:
self.run_ASR = self.run_ASR_BPE_CTC
asr_model = EncDecCTCModelBPE.from_pretrained(model_name=asr_model,
strict=False)
self.model_stride_in_secs = 0.04
self.asr_delay_sec = 0.0
self.params['offset'] = 0
self.chunk_len_in_sec = 1.6
self.total_buffer_in_secs = 4
elif 'citrinet' in asr_model:
self.run_ASR = self.run_ASR_BPE_CTC
asr_model = EncDecCTCModelBPE.from_pretrained(model_name=asr_model,
strict=False)
self.model_stride_in_secs = 0.08
self.asr_delay_sec = 0.0
self.params['offset'] = 0
self.chunk_len_in_sec = 1.6
self.total_buffer_in_secs = 4
elif 'conformer_transducer' in asr_model or 'contextnet' in asr_model:
self.run_ASR = self.run_ASR_BPE_RNNT
asr_model = EncDecRNNTBPEModel.from_pretrained(
model_name=asr_model, strict=False)
self.model_stride_in_secs = 0.04
self.asr_delay_sec = 0.0
self.params['offset'] = 0
self.chunk_len_in_sec = 1.6
self.total_buffer_in_secs = 4
else:
raise ValueError(f"ASR model name not found: {asr_model}")
self.params['time_stride'] = self.model_stride_in_secs
self.asr_batch_size = 16
asr_model.eval()
self.audio_file_list = [
value['audio_filepath']
for _, value in self.AUDIO_RTTM_MAP.items()
]
return asr_model
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 set_asr_model(self, ASR_model_name):
if 'QuartzNet' in ASR_model_name:
self.run_ASR = self.run_ASR_QuartzNet_CTC
asr_model = EncDecCTCModel.from_pretrained(
model_name=ASR_model_name, strict=False)
elif 'conformer' in ASR_model_name:
self.run_ASR = self.run_ASR_Conformer_CTC
_ = EncDecCTCModelBPE.from_pretrained(model_name=ASR_model_name,
strict=False)
raise NotImplementedError
# This option has not been implemented yet.
elif 'citrinet' in ASR_model_name:
raise NotImplementedError
else:
raise ValueError(
f"ASR model name not found: {self.params['ASR_model_name']}")
return asr_model
def batch_inference(args: argparse.Namespace):
torch.set_grad_enabled(False)
if args.asr_model.endswith(".nemo"):
print(f"Using local ASR model from {args.asr_model}")
asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model)
else:
print(f"Using NGC cloud ASR model {args.asr_model}")
asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model)
manifest = prepare_manifest(args.corpora_dir, args.limit)
asr_model.setup_test_data(
test_data_config={
"sample_rate": 16000,
"manifest_filepath": manifest,
"labels": asr_model.decoder.vocabulary,
"batch_size": args.batch_size,
"normalize_transcripts": args.normalize_text,
})
refs_hyps = list(tqdm(generate_ref_hyps(asr_model, args.search,
args.arpa)))
references, hypotheses = [list(k) for k in zip(*refs_hyps)]
os.makedirs(args.results_dir, exist_ok=True)
data_io.write_lines(f"{args.results_dir}/refs.txt.gz", references)
data_io.write_lines(f"{args.results_dir}/hyps.txt.gz", hypotheses)
wer_value = word_error_rate(hypotheses=hypotheses, references=references)
sys.stdout.flush()
stats = {
"wer": wer_value,
"args": args.__dict__,
}
data_io.write_json(f"{args.results_dir}/stats.txt", stats)
print(f"Got WER of {wer_value}")
return stats
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model",
type=str,
default="QuartzNet15x5Base-En",
choices=[
x.pretrained_model_name
for x in EncDecCTCModel.list_available_models()
],
)
parser.add_argument(
"--tts_model_spec",
type=str,
default="Tacotron2-22050Hz",
choices=[
x.pretrained_model_name
for x in SpectrogramGenerator.list_available_models()
],
)
parser.add_argument(
"--tts_model_vocoder",
type=str,
default="WaveGlow-22050Hz",
choices=[
x.pretrained_model_name for x in Vocoder.list_available_models()
],
)
parser.add_argument("--wer_tolerance",
type=float,
default=1.0,
help="used by test")
parser.add_argument("--trim", action="store_true")
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
torch.set_grad_enabled(False)
if args.debug:
logging.set_verbosity(logging.DEBUG)
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model)
logging.info(
f"Using NGC cloud TTS Spectrogram Generator model {args.tts_model_spec}"
)
tts_model_spec = SpectrogramGenerator.from_pretrained(
model_name=args.tts_model_spec)
logging.info(f"Using NGC cloud TTS Vocoder model {args.tts_model_vocoder}")
tts_model_vocoder = Vocoder.from_pretrained(
model_name=args.tts_model_vocoder)
models = [asr_model, tts_model_spec, tts_model_vocoder]
if torch.cuda.is_available():
for i, m in enumerate(models):
models[i] = m.cuda()
for m in models:
m.eval()
asr_model, tts_model_spec, tts_model_vocoder = models
parser = parsers.make_parser(
labels=asr_model.decoder.vocabulary,
name="en",
unk_id=-1,
blank_id=-1,
do_normalize=True,
)
labels_map = dict([(i, asr_model.decoder.vocabulary[i])
for i in range(len(asr_model.decoder.vocabulary))])
tts_input = []
asr_references = []
longest_tts_input = 0
for test_str in LIST_OF_TEST_STRINGS:
tts_parsed_input = tts_model_spec.parse(test_str)
if len(tts_parsed_input[0]) > longest_tts_input:
longest_tts_input = len(tts_parsed_input[0])
tts_input.append(tts_parsed_input.squeeze())
asr_parsed = parser(test_str)
asr_parsed = ''.join([labels_map[c] for c in asr_parsed])
asr_references.append(asr_parsed)
# Pad TTS Inputs
for i, text in enumerate(tts_input):
pad = (0, longest_tts_input - len(text))
tts_input[i] = torch.nn.functional.pad(text, pad, value=68)
logging.debug(tts_input)
# Do TTS
tts_input = torch.stack(tts_input)
if torch.cuda.is_available():
tts_input = tts_input.cuda()
specs = tts_model_spec.generate_spectrogram(tokens=tts_input)
audio = []
step = ceil(len(specs) / 4)
for i in range(4):
audio.append(
tts_model_vocoder.convert_spectrogram_to_audio(
spec=specs[i * step:i * step + step]))
audio = [item for sublist in audio for item in sublist]
audio_file_paths = []
# Save audio
logging.debug(f"args.trim: {args.trim}")
for i, aud in enumerate(audio):
aud = aud.cpu().numpy()
if args.trim:
aud = librosa.effects.trim(aud, top_db=40)[0]
librosa.output.write_wav(f"{i}.wav", aud, sr=22050)
audio_file_paths.append(str(Path(f"{i}.wav")))
# Do ASR
hypotheses = asr_model.transcribe(audio_file_paths)
for i, _ in enumerate(hypotheses):
logging.debug(f"{i}")
logging.debug(f"ref:'{asr_references[i]}'")
logging.debug(f"hyp:'{hypotheses[i]}'")
wer_value = word_error_rate(hypotheses=hypotheses,
references=asr_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 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("--dataset",
type=str,
required=True,
help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument(
"--normalize_text",
default=True,
type=bool,
help="Normalize transcripts or not. Set to False for non-English.")
parser.add_argument('--num_calib_batch',
default=1,
type=int,
help="Number of batches for calibration.")
parser.add_argument('--calibrator',
type=str,
choices=["max", "histogram"],
default="max")
parser.add_argument('--percentile',
nargs='+',
type=float,
default=[99.9, 99.99, 99.999, 99.9999])
parser.add_argument("--amp",
action="store_true",
help="Use AMP in calibration.")
parser.set_defaults(amp=False)
args = parser.parse_args()
torch.set_grad_enabled(False)
# Initialize quantization
quant_desc_input = QuantDescriptor(calib_method=args.calibrator)
quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input)
quant_nn.QuantConvTranspose2d.set_default_quant_desc_input(
quant_desc_input)
quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input)
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.normalize_text,
'shuffle': True,
})
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
# Enable calibrators
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
for i, test_batch in enumerate(asr_model.test_dataloader()):
if can_gpu:
test_batch = [x.cuda() for x in test_batch]
if args.amp:
with autocast():
_ = asr_model(input_signal=test_batch[0],
input_signal_length=test_batch[1])
else:
_ = asr_model(input_signal=test_batch[0],
input_signal_length=test_batch[1])
if i >= args.num_calib_batch:
break
# Save calibrated model(s)
model_name = args.asr_model.replace(
".nemo", "") if args.asr_model.endswith(".nemo") else args.asr_model
if not args.calibrator == "histogram":
compute_amax(asr_model, method="max")
asr_model.save_to(
F"{model_name}-max-{args.num_calib_batch*args.batch_size}.nemo")
else:
for percentile in args.percentile:
print(F"{percentile} percentile calibration")
compute_amax(asr_model, method="percentile")
asr_model.save_to(
F"{model_name}-percentile-{percentile}-{args.num_calib_batch*args.batch_size}.nemo"
)
for method in ["mse", "entropy"]:
print(F"{method} calibration")
compute_amax(asr_model, method=method)
asr_model.save_to(
F"{model_name}-{method}-{args.num_calib_batch*args.batch_size}.nemo"
)
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)
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()
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("--num_batch",
type=int,
default=50,
help="number of batches of the synthetic data")
parser.add_argument("--batch_size",
type=int,
default=8,
help="batch size of the synthetic data")
parser.add_argument("--seqlen",
type=int,
default=500,
help="sequence length of the synthetic data")
parser.add_argument(
"--train_iter",
type=int,
default=200,
help="training iterations for the synthetic data generation")
parser.add_argument("--dump_path",
type=str,
default=None,
help="path to dump the synthetic data")
parser.add_argument(
"--dump_prefix",
type=str,
default='syn',
help="prefix for the filename of the dumped synthetic data")
parser.add_argument("--lr",
type=float,
default=0.01,
help="Learning rate for the synthetic data generation")
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}")
teacher_model = EncDecCTCModel.restore_from(
restore_path=args.asr_model)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
teacher_model = EncDecCTCModel.from_pretrained(
model_name=args.asr_model)
teacher_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': teacher_model.decoder.vocabulary,
'batch_size': 8,
'normalize_transcripts': True,
'shuffle': True,
})
############################## Distillation #####################################
teacher_model.set_quant_mode(
'none') # distable quantization mode for the teacher model
torch.set_grad_enabled(True) # enable backward graph generation
print("Num batches: %d, Batch size: %d, Training iterations: %d, Learning rate: %.3f " \
% (args.num_batch, args.batch_size, args.train_iter, args.lr))
print('Synthesizing...')
synthetic_data = get_synthetic_data(teacher_model.encoder,
teacher_model.decoder,
batch_size=args.batch_size,
dim=64,
seqlen=args.seqlen,
num_batch=args.num_batch,
train_iter=args.train_iter,
lr=args.lr)
file_name = '%s_nb%d_iter%d_lr%.3f.pkl' % \
(args.dump_prefix, args.num_batch, args.train_iter, args.lr)
if args.dump_path is not None:
if not os.path.exists(args.dump_path):
os.makedirs(args.dump_path)
file_name = os.path.join(args.dump_path, file_name)
print('Synthetic data dumped as ', file_name)
with open(file_name, 'wb') as f:
pickle.dump([x.cpu() for x in synthetic_data], f)
