#!/usr/bin/env python3
import argparse
import logging
import sys
from pathlib import Path
from typing import Any, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from typeguard import typechecked
from espnet2.asr.transducer.beam_search_transducer import BeamSearchTransducer
from espnet2.asr.transducer.beam_search_transducer import Hypothesis as TransHypothesis
from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.tasks.enh_s2t import EnhS2TTask
from espnet2.tasks.lm import LMTask
from espnet2.tasks.st import STTask
from espnet2.text.build_tokenizer import build_tokenizer
from espnet2.text.token_id_converter import TokenIDConverter
from espnet2.text.whisper_token_id_converter import OpenAIWhisperTokenIDConverter
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.utils import config_argparse
from espnet2.utils.types import str2bool, str2triple_str, str_or_none
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.beam_search import BeamSearch, Hypothesis
from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
from espnet.nets.scorer_interface import BatchScorerInterface
from espnet.nets.scorers.ctc import CTCPrefixScorer
from espnet.nets.scorers.length_bonus import LengthBonus
from espnet.utils.cli_utils import get_commandline_args
try:
from transformers import AutoModelForSeq2SeqLM
is_transformers_available = True
except ImportError:
is_transformers_available = False
[docs]class Speech2Text:
"""Speech2Text class
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("st_config.yml", "st.pth")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]
"""
@typechecked
def __init__(
self,
st_train_config: Union[Path, str, None] = None,
st_model_file: Union[Path, str, None] = None,
transducer_conf: Optional[dict] = None,
lm_train_config: Union[Path, str, None] = None,
lm_file: Union[Path, str, None] = None,
ngram_scorer: str = "full",
ngram_file: Union[Path, str, None] = None,
token_type: Optional[str] = None,
bpemodel: Optional[str] = None,
src_lm_train_config: Union[Path, str, None] = None,
src_lm_file: Union[Path, str, None] = None,
src_ngram_scorer: str = "full",
src_ngram_file: Union[Path, str, None] = None,
src_token_type: Optional[str] = None,
src_bpemodel: Optional[str] = None,
device: str = "cpu",
maxlenratio: float = 0.0,
minlenratio: float = 0.0,
asr_maxlenratio: float = 0.0,
asr_minlenratio: float = 0.0,
batch_size: int = 1,
dtype: str = "float32",
beam_size: int = 20,
ctc_weight: float = 0.0,
lm_weight: float = 1.0,
ngram_weight: float = 0.9,
penalty: float = 0.0,
nbest: int = 1,
normalize_length: bool = False,
asr_beam_size: int = 20,
asr_lm_weight: float = 1.0,
asr_ngram_weight: float = 0.9,
asr_penalty: float = 0.0,
asr_ctc_weight: float = 0.3,
asr_nbest: int = 1,
enh_s2t_task: bool = False,
ctc_greedy: bool = False,
hugging_face_decoder: bool = False,
hugging_face_decoder_max_length: int = 256,
):
task = STTask if not enh_s2t_task else EnhS2TTask
# 1. Build ST model
scorers = {}
asr_scorers = {}
st_model, st_train_args = task.build_model_from_file(
st_train_config, st_model_file, device
)
if enh_s2t_task:
st_model.inherite_attributes(
inherite_s2t_attrs=[
"ctc",
"decoder",
"eos",
"joint_network",
"sos",
"token_list",
"use_transducer_decoder",
]
)
st_model.to(dtype=getattr(torch, dtype)).eval()
if hasattr(st_model, "decoder"):
decoder = st_model.decoder
else:
decoder = None
token_list = st_model.token_list
scorers.update(
decoder=decoder,
length_bonus=LengthBonus(len(token_list)),
)
if ctc_weight > 0:
assert hasattr(st_model, "st_ctc")
ctc = CTCPrefixScorer(ctc=st_model.st_ctc, eos=st_model.eos)
scorers.update(ctc=ctc)
src_token_list = st_model.src_token_list
if st_model.use_multidecoder:
asr_decoder = st_model.extra_asr_decoder
asr_ctc = CTCPrefixScorer(ctc=st_model.ctc, eos=st_model.src_eos)
asr_scorers.update(
decoder=asr_decoder,
ctc=asr_ctc,
length_bonus=LengthBonus(len(src_token_list)),
)
else:
asr_decoder = None
# 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
if src_lm_train_config is not None:
src_lm, src_lm_train_args = LMTask.build_model_from_file(
src_lm_train_config, src_lm_file, device
)
asr_scorers["lm"] = src_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
if src_ngram_file is not None:
if src_ngram_scorer == "full":
from espnet.nets.scorers.ngram import NgramFullScorer
src_ngram = NgramFullScorer(src_ngram_file, src_token_list)
else:
from espnet.nets.scorers.ngram import NgramPartScorer
src_ngram = NgramPartScorer(src_ngram_file, src_token_list)
else:
src_ngram = None
asr_scorers["ngram"] = src_ngram
# 4. Build BeamSearch object
if st_model.st_use_transducer_decoder:
beam_search_transducer = BeamSearchTransducer(
decoder=st_model.decoder,
joint_network=st_model.st_joint_network,
beam_size=beam_size,
lm=scorers["lm"] if "lm" in scorers else None,
lm_weight=lm_weight,
token_list=token_list,
**transducer_conf,
)
beam_search = None
hugging_face_model = None
hugging_face_linear_in = None
elif (
decoder.__class__.__name__ == "HuggingFaceTransformersDecoder"
and hugging_face_decoder
):
if not is_transformers_available:
raise ImportError(
"`transformers` is not available."
" Please install it via `pip install transformers`"
" or `cd /path/to/espnet/tools && . ./activate_python.sh"
" && ./installers/install_transformers.sh`."
)
hugging_face_model = AutoModelForSeq2SeqLM.from_pretrained(
decoder.model_name_or_path
)
hugging_face_model.lm_head.load_state_dict(decoder.lm_head.state_dict())
if hasattr(hugging_face_model, "model"):
hugging_face_model.model.decoder.load_state_dict(
decoder.decoder.state_dict()
)
del hugging_face_model.model.encoder
else:
hugging_face_model.decoder.load_state_dict(decoder.decoder.state_dict())
del hugging_face_model.encoder
# del st_model.decoder.lm_head
# del st_model.decoder.decoder
hugging_face_linear_in = decoder.linear_in
hugging_face_model.to(device=device).eval()
# hacky way to use .score()
st_model.decoder.hf_generate = hugging_face_model
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=hugging_face_model.config.decoder_start_token_id,
eos=hugging_face_model.config.eos_token_id,
vocab_size=len(token_list),
token_list=token_list,
pre_beam_score_key="full",
normalize_length=normalize_length,
)
# beam_search = None
beam_search_transducer = None
# 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}")
else:
beam_search_transducer = None
hugging_face_model = None
hugging_face_linear_in = 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=st_model.sos,
eos=st_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}")
asr_weights = dict(
decoder=1.0 - asr_ctc_weight,
ctc=asr_ctc_weight,
lm=asr_lm_weight,
ngram=asr_ngram_weight,
length_bonus=asr_penalty,
)
asr_beam_search = BeamSearch(
beam_size=asr_beam_size,
weights=asr_weights,
scorers=asr_scorers,
sos=st_model.src_sos,
eos=st_model.src_eos,
vocab_size=len(src_token_list),
token_list=src_token_list,
pre_beam_score_key="full",
return_hs=True,
)
# TODO(karita): make all scorers batchfied
if batch_size == 1:
non_batch = [
k
for k, v in asr_beam_search.full_scorers.items()
if not isinstance(v, BatchScorerInterface)
]
if len(non_batch) == 0:
asr_beam_search.__class__ = BatchBeamSearch
logging.info("BatchBeamSearch implementation is selected for ASR.")
else:
logging.warning(
f"As non-batch scorers {non_batch} are found, "
f"fall back to non-batch implementation."
)
asr_beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
for scorer in asr_scorers.values():
if isinstance(scorer, torch.nn.Module):
scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
logging.info(f"ASR Beam_search: {asr_beam_search}")
logging.info(f"Decoding device={device}, dtype={dtype}")
# 4. [Optional] Build Text converter: e.g. bpe-sym -> Text
# compatibility for whisper tokenizer
preprocessor_conf = getattr(st_train_args, "preprocessor_conf", {})
whisper_language = preprocessor_conf.get("whisper_language", None)
whisper_task = preprocessor_conf.get("whisper_task", None)
if whisper_language:
src_token_lang, token_lang = whisper_language
else:
src_token_lang, token_lang = None, None
if token_type is None:
token_type = st_train_args.token_type
if bpemodel is None:
bpemodel = st_train_args.bpemodel
if token_type is None:
tokenizer = None
elif (
token_type == "bpe"
or token_type == "hugging_face"
or "whisper" in token_type
):
if bpemodel is not None:
tokenizer = build_tokenizer(
token_type=token_type,
bpemodel=bpemodel,
whisper_language=token_lang,
whisper_task=whisper_task,
)
else:
tokenizer = None
else:
tokenizer = build_tokenizer(token_type=token_type)
if "whisper" in token_type:
converter = OpenAIWhisperTokenIDConverter(
model_type=bpemodel,
language=token_lang or "en",
task=whisper_task or "translate",
)
beam_search.set_hyp_primer(
list(converter.tokenizer.sot_sequence_including_notimestamps)
)
else:
converter = TokenIDConverter(token_list=token_list)
logging.info(f"Text tokenizer: {tokenizer}")
if src_token_type is None:
src_token_type = st_train_args.src_token_type
if src_bpemodel is None:
src_bpemodel = st_train_args.src_bpemodel
if src_token_type is None:
src_tokenizer = None
elif src_token_type == "bpe" or "whisper" in token_type:
if src_bpemodel is not None:
src_tokenizer = build_tokenizer(
token_type=src_token_type,
bpemodel=src_bpemodel,
whisper_language=src_token_lang,
whisper_task=whisper_task,
)
else:
src_tokenizer = None
else:
src_tokenizer = build_tokenizer(token_type=src_token_type)
if "whisper" in src_token_type:
src_converter = OpenAIWhisperTokenIDConverter(
model_type=src_bpemodel,
language=src_token_lang or "en",
task=whisper_task or "translate",
)
asr_beam_search.set_hyp_primer(
list(src_converter.tokenizer.sot_sequence_including_notimestamps)
)
else:
src_converter = TokenIDConverter(token_list=src_token_list)
logging.info(f"Src Text tokenizer: {src_tokenizer}")
self.st_model = st_model
self.st_train_args = st_train_args
self.converter = converter
self.tokenizer = tokenizer
self.src_converter = src_converter
self.src_tokenizer = src_tokenizer
self.beam_search = beam_search
self.beam_search_transducer = beam_search_transducer
self.hugging_face_model = hugging_face_model
self.hugging_face_linear_in = hugging_face_linear_in
self.hugging_face_beam_size = beam_size
self.hugging_face_decoder_max_length = hugging_face_decoder_max_length
self.maxlenratio = maxlenratio
self.minlenratio = minlenratio
self.asr_beam_search = asr_beam_search
self.asr_maxlenratio = asr_maxlenratio
self.asr_minlenratio = asr_minlenratio
self.device = device
self.dtype = dtype
self.nbest = nbest
self.asr_nbest = asr_nbest
self.ctc_greedy = ctc_greedy
@torch.no_grad()
@typechecked
def __call__(
self, speech: Union[torch.Tensor, np.ndarray]
) -> List[
Tuple[Optional[str], List[str], List[int], Union[Hypothesis, TransHypothesis]]
]:
"""Inference
Args:
data: Input speech data
Returns:
text, token, token_int, hyp
"""
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
# data: (Nsamples,) -> (1, Nsamples)
speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
# lengths: (1,)
lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
batch = {"speech": speech, "speech_lengths": lengths}
# a. To device
batch = to_device(batch, device=self.device)
# b. Forward Encoder
enc, _, asr_enc, _ = self.st_model.encode(**batch, return_int_enc=True)
assert len(enc) == 1, len(enc)
x = enc[0]
# Multi-decoder ASR beam search
if self.st_model.use_multidecoder:
asr_nbest_hyps = self.asr_beam_search(
x=asr_enc[0],
maxlenratio=self.asr_maxlenratio,
minlenratio=self.asr_minlenratio,
)
asr_results = []
for hyp in asr_nbest_hyps:
assert isinstance(hyp, Hypothesis), type(hyp)
# remove sos/eos and get results
if isinstance(hyp.hs, List):
asr_hs = torch.stack(hyp.hs)
else:
asr_hs = hyp.hs
src_token_int = hyp.yseq.tolist()
src_token_int = list(
filter(lambda x: x != self.st_model.src_sos, src_token_int)
)
src_token_int = list(
filter(lambda x: x != self.st_model.src_eos, src_token_int)
)
# remove blank symbol id, which is assumed to be 0
src_token_int = list(filter(lambda x: x != 0, src_token_int))
# Change integer-ids to tokens
src_token = self.src_converter.ids2tokens(src_token_int)
if self.src_tokenizer is not None:
src_hyp_text = self.src_tokenizer.tokens2text(src_token)
else:
src_hyp_text = None
asr_results.append(
(src_hyp_text, src_token, src_token_int, hyp, asr_hs)
)
# Encode 1 best ASR result
asr_hs = asr_results[0][-1].unsqueeze(0)
asr_hs = to_device(asr_hs, device=self.device)
asr_hs_lengths = asr_hs.new_full(
[1], dtype=torch.long, fill_value=asr_hs.size(1)
)
md_enc, _, _ = self.st_model.md_encoder(asr_hs, asr_hs_lengths)
x = md_enc[0]
pre_x = enc[0]
# c. Passed the encoder result and the beam search
if self.ctc_greedy:
from itertools import groupby
lpz = self.st_model.st_ctc.argmax(enc)
collapsed_indices = [x[0] for x in groupby(lpz[0])]
hyp = [x for x in filter(lambda x: x != 0, collapsed_indices)]
nbest_hyps = [
{"score": 0.0, "yseq": [self.st_model.sos] + hyp + [self.st_model.eos]}
]
nbest_hyps = [
Hypothesis(
score=hyp["score"],
yseq=torch.tensor(hyp["yseq"]),
)
for hyp in nbest_hyps
]
elif self.st_model.use_multidecoder and self.st_model.use_speech_attn:
nbest_hyps = self.beam_search(
x=x,
maxlenratio=self.maxlenratio,
minlenratio=self.minlenratio,
pre_x=pre_x,
)
elif self.beam_search_transducer:
logging.info("encoder output length: " + str(x.shape[0]))
nbest_hyps = self.beam_search_transducer(x)
best = nbest_hyps[0]
logging.info(f"total log probability: {best.score:.2f}")
logging.info(
f"normalized log probability: {best.score / len(best.yseq):.2f}"
)
logging.info(
"best hypo: " + "".join(self.converter.ids2tokens(best.yseq[1:])) + "\n"
)
else:
nbest_hyps = self.beam_search(
x=x, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
)
nbest_hyps = nbest_hyps[: self.nbest]
results = []
for hyp in nbest_hyps:
assert isinstance(hyp, (Hypothesis, TransHypothesis)), type(hyp)
# remove sos/eos and get results
last_pos = None if self.st_model.st_use_transducer_decoder else -1
if isinstance(hyp.yseq, list):
token_int = hyp.yseq[1:last_pos]
else:
token_int = hyp.yseq[1:last_pos].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 = self.converter.ids2tokens(token_int)
if self.tokenizer is not None:
text = self.tokenizer.tokens2text(token)
else:
text = None
results.append((text, token, token_int, hyp))
if self.st_model.use_multidecoder:
return (results, asr_results)
return results
[docs] @staticmethod
def from_pretrained(
model_tag: Optional[str] = None,
**kwargs: Optional[Any],
):
"""Build Speech2Text instance from the pretrained model.
Args:
model_tag (Optional[str]): Model tag of the pretrained models.
Currently, the tags of espnet_model_zoo are supported.
Returns:
Speech2Text: Speech2Text instance.
"""
if model_tag is not None:
try:
from espnet_model_zoo.downloader import ModelDownloader
except ImportError:
logging.error(
"`espnet_model_zoo` is not installed. "
"Please install via `pip install -U espnet_model_zoo`."
)
raise
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return Speech2Text(**kwargs)
[docs]@typechecked
def inference(
output_dir: str,
maxlenratio: float,
minlenratio: float,
asr_maxlenratio: float,
asr_minlenratio: float,
batch_size: int,
dtype: str,
beam_size: int,
asr_beam_size: int,
ngpu: int,
seed: int,
ctc_weight: float,
lm_weight: float,
ngram_weight: float,
penalty: float,
nbest: int,
normalize_length: bool,
asr_ctc_weight: float,
asr_lm_weight: float,
asr_ngram_weight: float,
asr_penalty: float,
asr_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],
st_train_config: Optional[str],
st_model_file: Optional[str],
lm_train_config: Optional[str],
lm_file: Optional[str],
word_lm_train_config: Optional[str],
word_lm_file: Optional[str],
ngram_file: Optional[str],
src_lm_train_config: Optional[str],
src_lm_file: Optional[str],
src_word_lm_train_config: Optional[str],
src_word_lm_file: Optional[str],
src_ngram_file: Optional[str],
model_tag: Optional[str],
token_type: Optional[str],
bpemodel: Optional[str],
src_token_type: Optional[str],
src_bpemodel: Optional[str],
allow_variable_data_keys: bool,
transducer_conf: Optional[dict],
enh_s2t_task: bool,
ctc_greedy: bool,
hugging_face_decoder: bool,
hugging_face_decoder_max_length: int,
):
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_kwargs = dict(
st_train_config=st_train_config,
st_model_file=st_model_file,
transducer_conf=transducer_conf,
lm_train_config=lm_train_config,
lm_file=lm_file,
ngram_file=ngram_file,
src_lm_train_config=src_lm_train_config,
src_lm_file=src_lm_file,
src_ngram_file=src_ngram_file,
token_type=token_type,
bpemodel=bpemodel,
src_token_type=src_token_type,
src_bpemodel=src_bpemodel,
device=device,
maxlenratio=maxlenratio,
minlenratio=minlenratio,
asr_maxlenratio=asr_maxlenratio,
asr_minlenratio=asr_minlenratio,
dtype=dtype,
beam_size=beam_size,
ctc_weight=ctc_weight,
lm_weight=lm_weight,
ngram_weight=ngram_weight,
penalty=penalty,
nbest=nbest,
normalize_length=normalize_length,
asr_beam_size=asr_beam_size,
asr_ctc_weight=asr_ctc_weight,
asr_lm_weight=asr_lm_weight,
asr_ngram_weight=asr_ngram_weight,
asr_penalty=asr_penalty,
asr_nbest=asr_nbest,
enh_s2t_task=enh_s2t_task,
ctc_greedy=ctc_greedy,
hugging_face_decoder=hugging_face_decoder,
hugging_face_decoder_max_length=hugging_face_decoder_max_length,
)
speech2text = Speech2Text.from_pretrained(
model_tag=model_tag,
**speech2text_kwargs,
)
# 3. Build data-iterator
loader = STTask.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=STTask.build_preprocess_fn(speech2text.st_train_args, False),
collate_fn=STTask.build_collate_fn(speech2text.st_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")}
# N-best list of (text, token, token_int, hyp_object)
try:
results = speech2text(**batch)
# If multi-decoder, then also write ASR results
if len(results) == 2:
asr_results = results[-1]
results = results[0]
else:
asr_results = None
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
if asr_results is not None:
for n, (text, token, token_int, hyp, _) in zip(
range(1, asr_nbest + 1), asr_results
):
# Create a directory: outdir/{n}best_recog
ibest_writer = writer[f"{n}asr_best_recog"]
# Write the result to each file
ibest_writer["asr_token"][key] = " ".join(token)
ibest_writer["asr_token_int"][key] = " ".join(map(str, token_int))
ibest_writer["asr_score"][key] = str(hyp.score)
if text is not None:
ibest_writer["asr_text"][key] = text
[docs]def get_parser():
parser = config_argparse.ArgumentParser(
description="ST Decoding",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
# Note(kamo): Use '_' instead of '-' as separator.
# '-' is confusing if written in yaml.
parser.add_argument(
"--log_level",
type=lambda x: x.upper(),
default="INFO",
choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
help="The verbose level of logging",
)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument(
"--ngpu",
type=int,
default=0,
help="The number of gpus. 0 indicates CPU mode",
)
parser.add_argument("--seed", type=int, default=0, help="Random seed")
parser.add_argument(
"--dtype",
default="float32",
choices=["float16", "float32", "float64"],
help="Data type",
)
parser.add_argument(
"--num_workers",
type=int,
default=1,
help="The number of workers used for DataLoader",
)
group = parser.add_argument_group("Input data related")
group.add_argument(
"--data_path_and_name_and_type",
type=str2triple_str,
required=True,
action="append",
)
group.add_argument("--key_file", type=str_or_none)
group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
group = parser.add_argument_group("The model configuration related")
group.add_argument(
"--st_train_config",
type=str,
help="ST training configuration",
)
group.add_argument(
"--st_model_file",
type=str,
help="ST model parameter file",
)
group.add_argument(
"--lm_train_config",
type=str,
help="LM training configuration",
)
group.add_argument(
"--src_lm_train_config",
type=str,
help="LM training configuration",
)
group.add_argument(
"--lm_file",
type=str,
help="LM parameter file",
)
group.add_argument(
"--src_lm_file",
type=str,
help="LM parameter file",
)
group.add_argument(
"--word_lm_train_config",
type=str,
help="Word LM training configuration",
)
group.add_argument(
"--src_word_lm_train_config",
type=str,
help="Word LM training configuration",
)
group.add_argument(
"--word_lm_file",
type=str,
help="Word LM parameter file",
)
group.add_argument(
"--src_word_lm_file",
type=str,
help="Word LM parameter file",
)
group.add_argument(
"--ngram_file",
type=str,
help="N-gram parameter file",
)
group.add_argument(
"--src_ngram_file",
type=str,
help="N-gram parameter file",
)
group.add_argument(
"--model_tag",
type=str,
help="Pretrained model tag. If specify this option, *_train_config and "
"*_file will be overwritten",
)
group.add_argument(
"--enh_s2t_task",
type=str2bool,
default=False,
help="enhancement and asr joint model",
)
group = parser.add_argument_group("Beam-search related")
group.add_argument(
"--batch_size",
type=int,
default=1,
help="The batch size for inference",
)
group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
group.add_argument(
"--asr_nbest", type=int, default=1, help="Output N-best hypotheses"
)
group.add_argument("--beam_size", type=int, default=20, help="Beam size")
group.add_argument("--asr_beam_size", type=int, default=20, help="Beam size")
group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
group.add_argument(
"--asr_penalty", type=float, default=0.0, help="Insertion penalty"
)
group.add_argument(
"--maxlenratio",
type=float,
default=0.0,
help="Input length ratio to obtain max output length. "
"If maxlenratio=0.0 (default), it uses a end-detect "
"function "
"to automatically find maximum hypothesis lengths."
"If maxlenratio<0.0, its absolute value is interpreted"
"as a constant max output length",
)
group.add_argument(
"--asr_maxlenratio",
type=float,
default=0.0,
help="Input length ratio to obtain max output length. "
"If maxlenratio=0.0 (default), it uses a end-detect "
"function "
"to automatically find maximum hypothesis lengths."
"If maxlenratio<0.0, its absolute value is interpreted"
"as a constant max output length",
)
group.add_argument(
"--minlenratio",
type=float,
default=0.0,
help="Input length ratio to obtain min output length",
)
group.add_argument(
"--asr_minlenratio",
type=float,
default=0.0,
help="Input length ratio to obtain min output length",
)
group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
group.add_argument("--asr_lm_weight", type=float, default=1.0, help="RNNLM weight")
group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
group.add_argument(
"--asr_ngram_weight", type=float, default=0.9, help="ngram weight"
)
group.add_argument("--ctc_weight", type=float, default=0.0, help="ST CTC weight")
group.add_argument(
"--asr_ctc_weight", type=float, default=0.3, help="ASR CTC weight"
)
group.add_argument(
"--transducer_conf",
default=None,
help="The keyword arguments for transducer beam search.",
)
group = parser.add_argument_group("Text converter related")
group.add_argument(
"--token_type",
type=str_or_none,
default=None,
choices=["char", "bpe", None],
help="The token type for ST model. "
"If not given, refers from the training args",
)
group.add_argument(
"--src_token_type",
type=str_or_none,
default=None,
choices=["char", "bpe", None],
help="The token type for ST model. "
"If not given, refers from the training args",
)
group.add_argument(
"--bpemodel",
type=str_or_none,
default=None,
help="The model path of sentencepiece. "
"If not given, refers from the training args",
)
group.add_argument(
"--src_bpemodel",
type=str_or_none,
default=None,
help="The model path of sentencepiece. "
"If not given, refers from the training args",
)
group.add_argument(
"--ctc_greedy",
type=str2bool,
default=False,
)
group.add_argument("--hugging_face_decoder", type=str2bool, default=False)
group.add_argument("--hugging_face_decoder_max_length", type=int, default=256)
group.add_argument(
"--normalize_length",
type=str2bool,
default=False,
help="If true, best hypothesis is selected by length-normalized scores",
)
return parser
[docs]def main(cmd=None):
print(get_commandline_args(), file=sys.stderr)
parser = get_parser()
args = parser.parse_args(cmd)
kwargs = vars(args)
kwargs.pop("config", None)
inference(**kwargs)
if __name__ == "__main__":
main()