def inference(
output_dir: str,
maxlenratio: float,
minlenratio: float,
batch_size: int,
dtype: str,
beam_size: int,
ngpu: int,
seed: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
nbest: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
asr_train_config: str,
asr_model_file: str,
lm_train_config: Optional[str],
lm_file: Optional[str],
word_lm_train_config: Optional[str],
word_lm_file: Optional[str],
blank_symbol: str,
token_type: Optional[str],
bpemodel: Optional[str],
allow_variable_data_keys: bool,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if word_lm_train_config is not None:
raise NotImplementedError("Word LM is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build ASR model
scorers = {}
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, device)
asr_model.eval()
decoder = asr_model.decoder
ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
token_list = asr_model.token_list
scorers.update(
decoder=decoder,
ctc=ctc,
length_bonus=LengthBonus(len(token_list)),
)
# 3. Build Language model
if lm_train_config is not None:
lm, lm_train_args = LMTask.build_model_from_file(
lm_train_config, lm_file, device)
scorers["lm"] = lm.lm
# 4. Build BeamSearch object
weights = dict(
decoder=1.0 - ctc_weight,
ctc=ctc_weight,
lm=lm_weight,
length_bonus=penalty,
)
beam_search = BeamSearch(
beam_size=beam_size,
weights=weights,
scorers=scorers,
sos=asr_model.sos,
eos=asr_model.eos,
vocab_size=len(token_list),
token_list=token_list,
)
beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
for scorer in scorers.values():
if isinstance(scorer, torch.nn.Module):
scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
logging.info(f"Beam_search: {beam_search}")
logging.info(f"Decoding device={device}, dtype={dtype}")
# 5. Build data-iterator
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=ASRTask.build_preprocess_fn(asr_train_args, False),
collate_fn=ASRTask.build_collate_fn(asr_train_args),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 6. [Optional] Build Text converter: e.g. bpe-sym -> Text
if token_type is None:
token_type = asr_train_args.token_type
if bpemodel is None:
bpemodel = asr_train_args.bpemodel
if token_type is None:
tokenizer = None
elif token_type == "bpe":
if bpemodel is not None:
tokenizer = build_tokenizer(token_type=token_type,
bpemodel=bpemodel)
else:
tokenizer = None
else:
tokenizer = build_tokenizer(token_type=token_type)
converter = TokenIDConverter(token_list=token_list)
logging.info(f"Text tokenizer: {tokenizer}")
# 7 .Start for-loop
# FIXME(kamo): The output format should be discussed about
with DatadirWriter(output_dir) as writer:
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
with torch.no_grad():
# a. To device
batch = to_device(batch, device)
# b. Forward Encoder
enc, _ = asr_model.encode(**batch)
assert len(enc) == batch_size, len(enc)
# c. Passed the encoder result and the beam search
nbest_hyps = beam_search(x=enc[0],
maxlenratio=maxlenratio,
minlenratio=minlenratio)
nbest_hyps = nbest_hyps[:nbest]
# Only supporting batch_size==1
key = keys[0]
for n in range(1, nbest + 1):
hyp = nbest_hyps[n - 1]
assert isinstance(hyp, Hypothesis), type(hyp)
# remove sos/eos and get results
token_int = hyp.yseq[1:-1].tolist()
# remove blank symbol id, which is assumed to be 0
token_int = list(filter(lambda x: x != 0, token_int))
# Change integer-ids to tokens
token = converter.ids2tokens(token_int)
# Create a directory: outdir/{n}best_recog
ibest_writer = writer[f"{n}best_recog"]
# Write the result to each files
ibest_writer["token"][key] = " ".join(token)
ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["score"][key] = str(hyp.score)
if tokenizer is not None:
text = tokenizer.tokens2text(token)
ibest_writer["text"][key] = text
def inference(
output_dir: str,
maxlenratio: float,
minlenratio: float,
batch_size: int,
dtype: str,
beam_size: int,
ngpu: int,
seed: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
nbest: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
asr_train_config: str,
asr_model_file: str,
lm_train_config: Optional[str],
lm_file: Optional[str],
word_lm_train_config: Optional[str],
word_lm_file: Optional[str],
token_type: Optional[str],
bpemodel: Optional[str],
allow_variable_data_keys: bool,
sim_chunk_length: int,
disable_repetition_detection: bool,
encoded_feat_length_limit: int,
decoder_text_length_limit: int,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if word_lm_train_config is not None:
raise NotImplementedError("Word LM is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build speech2text
speech2text = Speech2TextStreaming(
asr_train_config=asr_train_config,
asr_model_file=asr_model_file,
lm_train_config=lm_train_config,
lm_file=lm_file,
token_type=token_type,
bpemodel=bpemodel,
device=device,
maxlenratio=maxlenratio,
minlenratio=minlenratio,
dtype=dtype,
beam_size=beam_size,
ctc_weight=ctc_weight,
lm_weight=lm_weight,
penalty=penalty,
nbest=nbest,
disable_repetition_detection=disable_repetition_detection,
decoder_text_length_limit=decoder_text_length_limit,
encoded_feat_length_limit=encoded_feat_length_limit,
)
# 3. Build data-iterator
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args,
False),
collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 7 .Start for-loop
# FIXME(kamo): The output format should be discussed about
with DatadirWriter(output_dir) as writer:
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
batch = {
k: v[0]
for k, v in batch.items() if not k.endswith("_lengths")
}
assert len(batch.keys()) == 1
try:
if sim_chunk_length == 0:
# N-best list of (text, token, token_int, hyp_object)
results = speech2text(**batch)
else:
speech = batch["speech"]
for i in range(len(speech) // sim_chunk_length):
speech2text(
speech=speech[i * sim_chunk_length:(i + 1) *
sim_chunk_length],
is_final=False,
)
results = speech2text(speech[(i + 1) *
sim_chunk_length:len(speech)],
is_final=True)
except TooShortUttError as e:
logging.warning(f"Utterance {keys} {e}")
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
results = [[" ", ["<space>"], [2], hyp]] * nbest
# Only supporting batch_size==1
key = keys[0]
for n, (text, token, token_int,
hyp) in zip(range(1, nbest + 1), results):
# Create a directory: outdir/{n}best_recog
ibest_writer = writer[f"{n}best_recog"]
# Write the result to each file
ibest_writer["token"][key] = " ".join(token)
ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["score"][key] = str(hyp.score)
if text is not None:
ibest_writer["text"][key] = text
def inference(
output_dir: str,
batch_size: int,
dtype: str,
ngpu: int,
seed: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
asr_train_config: str,
asr_model_file: str,
model_tag: Optional[str],
token_type: Optional[str],
bpemodel: Optional[str],
allow_variable_data_keys: bool,
maskctc_n_iterations: int,
maskctc_threshold_probability: float,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build speech2text
speech2text_kwargs = dict(
asr_train_config=asr_train_config,
asr_model_file=asr_model_file,
token_type=token_type,
bpemodel=bpemodel,
device=device,
batch_size=batch_size,
dtype=dtype,
maskctc_n_iterations=maskctc_n_iterations,
maskctc_threshold_probability=maskctc_threshold_probability,
)
speech2text = Speech2Text.from_pretrained(
model_tag=model_tag,
**speech2text_kwargs,
)
# 3. Build data-iterator
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args,
False),
collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 7 .Start for-loop
with DatadirWriter(output_dir) as writer:
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
batch = {
k: v[0]
for k, v in batch.items() if not k.endswith("_lengths")
}
try:
results = speech2text(**batch)
except TooShortUttError as e:
logging.warning(f"Utterance {keys} {e}")
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
results = [[" ", ["<space>"], [2], hyp]]
# Only supporting batch_size==1
key = keys[0]
(text, token, token_int, hyp) = results[0]
# Create a directory: outdir/{n}best_recog
ibest_writer = writer["1best_recog"]
# Write the result to each file
ibest_writer["token"][key] = " ".join(token)
ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["score"][key] = str(hyp.score)
if text is not None:
ibest_writer["text"][key] = text
def inference(
output_dir: str,
maxlenratio: float,
minlenratio: float,
batch_size: int,
dtype: str,
beam_size: int,
ngpu: int,
seed: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
nbest: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
asr_train_config: str,
asr_model_file: str,
lm_train_config: Optional[str],
lm_file: Optional[str],
word_lm_train_config: Optional[str],
word_lm_file: Optional[str],
token_type: Optional[str],
bpemodel: Optional[str],
allow_variable_data_keys: bool,
streaming: bool,
):
assert check_argument_types()
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build speech2text
speech2text = k2Speech2Text(
asr_train_config=asr_train_config,
asr_model_file=asr_model_file,
lm_train_config=lm_train_config,
lm_file=lm_file,
token_type=token_type,
bpemodel=bpemodel,
device=device,
maxlenratio=maxlenratio,
minlenratio=minlenratio,
dtype=dtype,
beam_size=beam_size,
ctc_weight=ctc_weight,
lm_weight=lm_weight,
penalty=penalty,
nbest=nbest,
streaming=streaming,
)
# 3. Build data-iterator
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args,
False),
collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
with DatadirWriter(output_dir) as writer:
for batch_idx, (keys, batch) in enumerate(loader):
if batch_idx % 10 == 0:
logging.info(f"Processing {batch_idx} batch")
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
# 1-best list of (text, token, token_int)
results = speech2text(batch)
for key_idx, (text, token, token_int, score) in enumerate(results):
key = keys[key_idx]
best_writer = writer["1best_recog"]
# Write the result to each file
best_writer["token"][key] = " ".join(token)
best_writer["token_int"][key] = " ".join(map(str, token_int))
best_writer["score"][key] = str(score)
if text is not None:
best_writer["text"][key] = text