def __init__(
self,
asr_train_config: Union[Path, str],
asr_model_file: Union[Path, str] = None,
lm_train_config: Union[Path, str] = None,
lm_file: Union[Path, str] = None,
token_type: str = None,
bpemodel: str = None,
device: str = "cpu",
maxlenratio: float = 0.0,
minlenratio: float = 0.0,
batch_size: int = 1,
dtype: str = "float32",
beam_size: int = 20,
ctc_weight: float = 0.5,
lm_weight: float = 1.0,
penalty: float = 0.0,
nbest: int = 1,
disable_repetition_detection=False,
decoder_text_length_limit=0,
encoded_feat_length_limit=0,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, device)
asr_model.to(dtype=getattr(torch, dtype)).eval()
assert isinstance(asr_model.encoder,
ContextualBlockTransformerEncoder) or isinstance(
asr_model.encoder,
ContextualBlockConformerEncoder)
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)),
)
# 2. 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
# 3. Build BeamSearch object
weights = dict(
decoder=1.0 - ctc_weight,
ctc=ctc_weight,
lm=lm_weight,
length_bonus=penalty,
)
assert "encoder_conf" in asr_train_args
assert "look_ahead" in asr_train_args.encoder_conf
assert "hop_size" in asr_train_args.encoder_conf
assert "block_size" in asr_train_args.encoder_conf
# look_ahead = asr_train_args.encoder_conf['look_ahead']
# hop_size = asr_train_args.encoder_conf['hop_size']
# block_size = asr_train_args.encoder_conf['block_size']
assert batch_size == 1
beam_search = BatchBeamSearchOnline(
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,
pre_beam_score_key=None if ctc_weight == 1.0 else "full",
disable_repetition_detection=disable_repetition_detection,
decoder_text_length_limit=decoder_text_length_limit,
encoded_feat_length_limit=encoded_feat_length_limit,
)
non_batch = [
k for k, v in beam_search.full_scorers.items()
if not isinstance(v, BatchScorerInterface)
]
assert len(non_batch) == 0
# TODO(karita): make all scorers batchfied
logging.info("BatchBeamSearchOnline implementation is selected.")
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}")
# 4. [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}")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.converter = converter
self.tokenizer = tokenizer
self.beam_search = beam_search
self.maxlenratio = maxlenratio
self.minlenratio = minlenratio
self.device = device
self.dtype = dtype
self.nbest = nbest
if "n_fft" in asr_train_args.frontend_conf:
self.n_fft = asr_train_args.frontend_conf["n_fft"]
else:
self.n_fft = 512
if "hop_length" in asr_train_args.frontend_conf:
self.hop_length = asr_train_args.frontend_conf["hop_length"]
else:
self.hop_length = 128
if ("win_length" in asr_train_args.frontend_conf
and asr_train_args.frontend_conf["win_length"] is not None):
self.win_length = asr_train_args.frontend_conf["win_length"]
else:
self.win_length = self.n_fft
self.reset()
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 __init__(
self,
asr_train_config: Union[Path, str],
asr_model_file: Union[Path, str] = None,
lm_train_config: Union[Path, str] = None,
lm_file: Union[Path, str] = None,
token_type: str = None,
bpemodel: str = None,
device: str = "cpu",
maxlenratio: float = 0.0,
minlenratio: float = 0.0,
batch_size: int = 1,
dtype: str = "float32",
beam_size: int = 20,
ctc_weight: float = 0.5,
lm_weight: float = 1.0,
penalty: float = 0.0,
nbest: int = 1,
streaming: bool = False,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, device)
asr_model.to(dtype=getattr(torch, dtype)).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)),
)
# 2. 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
# 3. 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,
pre_beam_score_key=None if ctc_weight == 1.0 else "full",
)
# TODO(karita): make all scorers batchfied
if batch_size == 1:
non_batch = [
k for k, v in beam_search.full_scorers.items()
if not isinstance(v, BatchScorerInterface)
]
if len(non_batch) == 0:
if streaming:
beam_search.__class__ = BatchBeamSearchOnlineSim
beam_search.set_streaming_config(asr_train_config)
logging.info(
"BatchBeamSearchOnlineSim implementation is selected.")
else:
beam_search.__class__ = BatchBeamSearch
logging.info("BatchBeamSearch implementation is selected.")
else:
logging.warning(f"As non-batch scorers {non_batch} are found, "
f"fall back to non-batch implementation.")
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}")
# 4. [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}")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.converter = converter
self.tokenizer = tokenizer
self.beam_search = beam_search
self.maxlenratio = maxlenratio
self.minlenratio = minlenratio
self.device = device
self.dtype = dtype
self.nbest = nbest
def calc_perplexity(
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],
train_config: Optional[str],
model_file: Optional[str],
log_base: Optional[float],
allow_variable_data_keys: bool,
):
assert check_argument_types()
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 LM
model, train_args = LMTask.build_model_from_file(train_config, model_file, device)
# Wrape model to make model.nll() data-parallel
wrapped_model = ForwardAdaptor(model, "nll")
wrapped_model.to(dtype=getattr(torch, dtype)).eval()
logging.info(f"Model:\n{model}")
# 3. Build data-iterator
loader = LMTask.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=LMTask.build_preprocess_fn(train_args, False),
collate_fn=LMTask.build_collate_fn(train_args, False),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 4. Start for-loop
with DatadirWriter(output_dir) as writer:
total_nll = 0.0
total_ntokens = 0
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():
batch = to_device(batch, device)
if ngpu <= 1:
# NOTE(kamo): data_parallel also should work with ngpu=1,
# but for debuggability it's better to keep this block.
nll, lengths = wrapped_model(**batch)
else:
nll, lengths = data_parallel(
wrapped_model, (), range(ngpu), module_kwargs=batch
)
assert _bs == len(nll) == len(lengths), (_bs, len(nll), len(lengths))
# nll: (B, L) -> (B,)
nll = nll.detach().cpu().numpy().sum(1)
# lengths: (B,)
lengths = lengths.detach().cpu().numpy()
total_nll += nll.sum()
total_ntokens += lengths.sum()
for key, _nll, ntoken in zip(keys, nll, lengths):
if log_base is None:
utt_ppl = np.exp(_nll / ntoken)
else:
utt_ppl = log_base ** (_nll / ntoken / np.log(log_base))
# Write PPL of each utts for debugging or analysis
writer["utt2ppl"][key] = str(utt_ppl)
writer["utt2ntokens"][key] = str(ntoken)
if log_base is None:
ppl = np.exp(total_nll / total_ntokens)
else:
ppl = log_base ** (total_nll / total_ntokens / np.log(log_base))
with (Path(output_dir) / "ppl").open("w", encoding="utf-8") as f:
f.write(f"{ppl}\n")
with (Path(output_dir) / "base").open("w", encoding="utf-8") as f:
if log_base is None:
_log_base = np.e
else:
_log_base = log_base
f.write(f"{_log_base}\n")
logging.info(f"PPL={ppl}")
def __init__(
self,
asr_train_config: Union[Path, str] = None,
asr_model_file: Union[Path, str] = None,
transducer_conf: dict = None,
lm_train_config: Union[Path, str] = None,
lm_file: Union[Path, str] = None,
ngram_scorer: str = "full",
ngram_file: Union[Path, str] = None,
token_type: str = None,
bpemodel: str = None,
device: str = "cpu",
maxlenratio: float = 0.0,
minlenratio: float = 0.0,
batch_size: int = 1,
dtype: str = "float32",
beam_size: int = 20,
ctc_weight: float = 0.5,
lm_weight: float = 1.0,
ngram_weight: float = 0.9,
penalty: float = 0.0,
nbest: int = 1,
streaming: bool = False,
enh_s2t_task: bool = False,
quantize_asr_model: bool = False,
quantize_lm: bool = False,
quantize_modules: List[str] = ["Linear"],
quantize_dtype: str = "qint8",
):
assert check_argument_types()
task = ASRTask if not enh_s2t_task else EnhS2TTask
if quantize_asr_model or quantize_lm:
if quantize_dtype == "float16" and torch.__version__ < LooseVersion(
"1.5.0"):
raise ValueError(
"float16 dtype for dynamic quantization is not supported with "
"torch version < 1.5.0. Switch to qint8 dtype instead.")
quantize_modules = set(
[getattr(torch.nn, q) for q in quantize_modules])
quantize_dtype = getattr(torch, quantize_dtype)
# 1. Build ASR model
scorers = {}
asr_model, asr_train_args = task.build_model_from_file(
asr_train_config, asr_model_file, device)
if enh_s2t_task:
asr_model.inherite_attributes(inherite_s2t_attrs=[
"ctc",
"decoder",
"eos",
"joint_network",
"sos",
"token_list",
"use_transducer_decoder",
])
asr_model.to(dtype=getattr(torch, dtype)).eval()
if quantize_asr_model:
logging.info("Use quantized asr model for decoding.")
asr_model = torch.quantization.quantize_dynamic(
asr_model, qconfig_spec=quantize_modules, dtype=quantize_dtype)
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)),
)
# 2. 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)
if quantize_lm:
logging.info("Use quantized lm for decoding.")
lm = torch.quantization.quantize_dynamic(
lm, qconfig_spec=quantize_modules, dtype=quantize_dtype)
scorers["lm"] = lm.lm
# 3. Build ngram model
if ngram_file is not None:
if ngram_scorer == "full":
from espnet.nets.scorers.ngram import NgramFullScorer
ngram = NgramFullScorer(ngram_file, token_list)
else:
from espnet.nets.scorers.ngram import NgramPartScorer
ngram = NgramPartScorer(ngram_file, token_list)
else:
ngram = None
scorers["ngram"] = ngram
# 4. Build BeamSearch object
if asr_model.use_transducer_decoder:
beam_search_transducer = BeamSearchTransducer(
decoder=asr_model.decoder,
joint_network=asr_model.joint_network,
beam_size=beam_size,
lm=scorers["lm"] if "lm" in scorers else None,
lm_weight=lm_weight,
**transducer_conf,
)
beam_search = None
else:
beam_search_transducer = None
weights = dict(
decoder=1.0 - ctc_weight,
ctc=ctc_weight,
lm=lm_weight,
ngram=ngram_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,
pre_beam_score_key=None if ctc_weight == 1.0 else "full",
)
# TODO(karita): make all scorers batchfied
if batch_size == 1:
non_batch = [
k for k, v in beam_search.full_scorers.items()
if not isinstance(v, BatchScorerInterface)
]
if len(non_batch) == 0:
if streaming:
beam_search.__class__ = BatchBeamSearchOnlineSim
beam_search.set_streaming_config(asr_train_config)
logging.info(
"BatchBeamSearchOnlineSim implementation is selected."
)
else:
beam_search.__class__ = BatchBeamSearch
logging.info(
"BatchBeamSearch implementation is selected.")
else:
logging.warning(
f"As non-batch scorers {non_batch} are found, "
f"fall back to non-batch implementation.")
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. [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}")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.converter = converter
self.tokenizer = tokenizer
self.beam_search = beam_search
self.beam_search_transducer = beam_search_transducer
self.maxlenratio = maxlenratio
self.minlenratio = minlenratio
self.device = device
self.dtype = dtype
self.nbest = nbest
def __init__(
self,
mt_train_config: Union[Path, str] = None,
mt_model_file: Union[Path, str] = None,
lm_train_config: Union[Path, str] = None,
lm_file: Union[Path, str] = None,
ngram_scorer: str = "full",
ngram_file: Union[Path, str] = None,
token_type: str = None,
bpemodel: str = None,
device: str = "cpu",
maxlenratio: float = 0.0,
minlenratio: float = 0.0,
batch_size: int = 1,
dtype: str = "float32",
beam_size: int = 20,
lm_weight: float = 1.0,
ngram_weight: float = 0.9,
penalty: float = 0.0,
nbest: int = 1,
):
assert check_argument_types()
# 1. Build MT model
scorers = {}
mt_model, mt_train_args = MTTask.build_model_from_file(
mt_train_config, mt_model_file, device)
mt_model.to(dtype=getattr(torch, dtype)).eval()
decoder = mt_model.decoder
token_list = mt_model.token_list
scorers.update(
decoder=decoder,
length_bonus=LengthBonus(len(token_list)),
)
# 2. 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
# 3. Build ngram model
if ngram_file is not None:
if ngram_scorer == "full":
from espnet.nets.scorers.ngram import NgramFullScorer
ngram = NgramFullScorer(ngram_file, token_list)
else:
from espnet.nets.scorers.ngram import NgramPartScorer
ngram = NgramPartScorer(ngram_file, token_list)
else:
ngram = None
scorers["ngram"] = ngram
# 4. Build BeamSearch object
weights = dict(
decoder=1.0,
lm=lm_weight,
ngram=ngram_weight,
length_bonus=penalty,
)
beam_search = BeamSearch(
beam_size=beam_size,
weights=weights,
scorers=scorers,
sos=mt_model.sos,
eos=mt_model.eos,
vocab_size=len(token_list),
token_list=token_list,
pre_beam_score_key="full",
)
# TODO(karita): make all scorers batchfied
if batch_size == 1:
non_batch = [
k for k, v in beam_search.full_scorers.items()
if not isinstance(v, BatchScorerInterface)
]
if len(non_batch) == 0:
beam_search.__class__ = BatchBeamSearch
logging.info("BatchBeamSearch implementation is selected.")
else:
logging.warning(f"As non-batch scorers {non_batch} are found, "
f"fall back to non-batch implementation.")
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}")
# 4. [Optional] Build Text converter: e.g. bpe-sym -> Text
if token_type is None:
token_type = mt_train_args.token_type
if bpemodel is None:
bpemodel = mt_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}")
self.mt_model = mt_model
self.mt_train_args = mt_train_args
self.converter = converter
self.tokenizer = tokenizer
self.beam_search = beam_search
self.maxlenratio = maxlenratio
self.minlenratio = minlenratio
self.device = device
self.dtype = dtype
self.nbest = nbest
def __init__(
self,
asr_train_config: Union[Path, str],
asr_model_file: Union[Path, str] = None,
lm_train_config: Union[Path, str] = None,
lm_file: Union[Path, str] = None,
token_type: str = None,
bpemodel: str = None,
device: str = "cpu",
maxlenratio: float = 0.0,
minlenratio: float = 0.0,
batch_size: int = 1,
dtype: str = "float32",
beam_size: int = 8,
ctc_weight: float = 0.5,
lm_weight: float = 1.0,
penalty: float = 0.0,
nbest: int = 1,
streaming: bool = False,
search_beam_size: int = 20,
output_beam_size: int = 20,
min_active_states: int = 30,
max_active_states: int = 10000,
blank_bias: float = 0.0,
lattice_weight: float = 1.0,
is_ctc_decoding: bool = True,
lang_dir: Optional[str] = None,
use_fgram_rescoring: bool = False,
use_nbest_rescoring: bool = False,
am_weight: float = 1.0,
decoder_weight: float = 0.5,
nnlm_weight: float = 1.0,
num_paths: int = 1000,
nbest_batch_size: int = 500,
nll_batch_size: int = 100,
):
assert check_argument_types()
# 1. Build ASR model
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, device
)
asr_model.to(dtype=getattr(torch, dtype)).eval()
token_list = asr_model.token_list
# 2. 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
)
self.lm = lm
self.is_ctc_decoding = is_ctc_decoding
self.use_fgram_rescoring = use_fgram_rescoring
self.use_nbest_rescoring = use_nbest_rescoring
assert self.is_ctc_decoding, "Currently, only ctc_decoding graph is supported."
if self.is_ctc_decoding:
self.decode_graph = k2.arc_sort(
build_ctc_topo(list(range(len(token_list))))
)
self.decode_graph = self.decode_graph.to(device)
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}")
logging.info(f"Running on : {device}")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.converter = converter
self.tokenizer = tokenizer
self.device = device
self.dtype = dtype
self.search_beam_size = search_beam_size
self.output_beam_size = output_beam_size
self.min_active_states = min_active_states
self.max_active_states = max_active_states
self.blank_bias = blank_bias
self.lattice_weight = lattice_weight
self.am_weight = am_weight
self.decoder_weight = decoder_weight
self.nnlm_weight = nnlm_weight
self.num_paths = num_paths
self.nbest_batch_size = nbest_batch_size
self.nll_batch_size = nll_batch_size
def __init__(
self,
asr_train_config: Union[Path, str],
asr_model_file: Union[Path, str] = None,
lm_train_config: Union[Path, str] = None,
lm_file: Union[Path, str] = None,
token_type: str = None,
bpemodel: str = None,
device: str = "cpu",
maxlenratio: float = 0.0,
minlenratio: float = 0.0,
dtype: str = "float32",
beam_size: int = 20,
ctc_weight: float = 0.5,
lm_weight: float = 1.0,
penalty: float = 0.0,
nbest: int = 1,
):
assert check_argument_types()
# 1. 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)),
)
# 2. 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
# 3. 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}")
# 4. [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}")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.lm_train_args = lm_train_args
self.converter = converter
self.tokenizer = tokenizer
self.beam_search = beam_search
self.maxlenratio = maxlenratio
self.minlenratio = minlenratio
self.device = device
self.dtype = dtype
self.nbest = nbest
def __init__(
self,
asr_train_config: Union[Path, str] = None,
asr_model_file: Union[Path, str] = None,
beam_search_config: Dict[str, Any] = None,
lm_train_config: Union[Path, str] = None,
lm_file: Union[Path, str] = None,
token_type: str = None,
bpemodel: str = None,
device: str = "cpu",
beam_size: int = 5,
dtype: str = "float32",
lm_weight: float = 1.0,
quantize_asr_model: bool = False,
quantize_modules: List[str] = None,
quantize_dtype: str = "qint8",
nbest: int = 1,
streaming: bool = False,
chunk_size: int = 16,
left_context: int = 32,
right_context: int = 0,
display_partial_hypotheses: bool = False,
) -> None:
assert check_argument_types()
asr_model, asr_train_args = ASRTransducerTask.build_model_from_file(
asr_train_config, asr_model_file, device)
if quantize_asr_model:
if quantize_modules is not None:
if not all([q in ["LSTM", "Linear"]
for q in quantize_modules]):
raise ValueError(
"Only 'Linear' and 'LSTM' modules are currently supported"
" by PyTorch and in --quantize_modules")
q_config = set(
[getattr(torch.nn, q) for q in quantize_modules])
else:
q_config = {torch.nn.Linear}
if quantize_dtype == "float16" and (V(torch.__version__) <
V("1.5.0")):
raise ValueError(
"float16 dtype for dynamic quantization is not supported with torch"
" version < 1.5.0. Switching to qint8 dtype instead.")
q_dtype = getattr(torch, quantize_dtype)
asr_model = torch.quantization.quantize_dynamic(
asr_model, q_config, dtype=q_dtype).eval()
else:
asr_model.to(dtype=getattr(torch, dtype)).eval()
if lm_train_config is not None:
lm, lm_train_args = LMTask.build_model_from_file(
lm_train_config, lm_file, device)
lm_scorer = lm.lm
else:
lm_scorer = None
# 4. Build BeamSearch object
if beam_search_config is None:
beam_search_config = {}
beam_search = BeamSearchTransducer(
asr_model.decoder,
asr_model.joint_network,
beam_size,
lm=lm_scorer,
lm_weight=lm_weight,
nbest=nbest,
**beam_search_config,
)
token_list = asr_model.token_list
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}")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.device = device
self.dtype = dtype
self.nbest = nbest
self.converter = converter
self.tokenizer = tokenizer
self.beam_search = beam_search
self.streaming = streaming
self.chunk_size = max(chunk_size, 0)
self.left_context = max(left_context, 0)
self.right_context = max(right_context, 0)
if not streaming or chunk_size == 0:
self.streaming = False
self.asr_model.encoder.dynamic_chunk_training = False
self.n_fft = asr_train_args.frontend_conf.get("n_fft", 512)
self.hop_length = asr_train_args.frontend_conf.get("hop_length", 128)
if asr_train_args.frontend_conf.get("win_length", None) is not None:
self.frontend_window_size = asr_train_args.frontend_conf[
"win_length"]
else:
self.frontend_window_size = self.n_fft
self.window_size = self.chunk_size + self.right_context
self._raw_ctx = self.asr_model.encoder.get_encoder_input_raw_size(
self.window_size, self.hop_length)
self.last_chunk_length = (self.asr_model.encoder.embed.min_frame_length
+ self.right_context + 1) * self.hop_length
self.reset_inference_cache()