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asr_inference_paraformer.py
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asr_inference_paraformer.py
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#!/usr/bin/env python3
import argparse
import logging
import sys
import time
import copy
import os
import codecs
import tempfile
import requests
from pathlib import Path
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
from typing import Dict
from typing import Any
from typing import List
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.fileio.datadir_writer import DatadirWriter
from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch
from funasr.modules.beam_search.beam_search import Hypothesis
from funasr.modules.scorers.ctc import CTCPrefixScorer
from funasr.modules.scorers.length_bonus import LengthBonus
from funasr.modules.subsampling import TooShortUttError
from funasr.tasks.asr import ASRTaskParaformer as ASRTask
from funasr.tasks.lm import LMTask
from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.token_id_converter import TokenIDConverter
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
from funasr.utils.cli_utils import get_commandline_args
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
class Speech2Text:
"""Speech2Text class
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]
"""
def __init__(
self,
asr_train_config: Union[Path, str] = None,
asr_model_file: Union[Path, str] = None,
cmvn_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,
ngram_weight: float = 0.9,
penalty: float = 0.0,
nbest: int = 1,
frontend_conf: dict = None,
hotword_list_or_file: str = None,
**kwargs,
):
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, cmvn_file, device
)
frontend = None
if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
logging.info("asr_model: {}".format(asr_model))
logging.info("asr_train_args: {}".format(asr_train_args))
asr_model.to(dtype=getattr(torch, dtype)).eval()
if asr_model.ctc != None:
ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
scorers.update(
ctc=ctc
)
token_list = asr_model.token_list
scorers.update(
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
# ngram is not supported now
ngram = None
scorers["ngram"] = ngram
# 4. Build BeamSearch object
# transducer is not supported now
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",
)
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"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
# 6. [Optional] Build hotword list from str, local file or url
# for None
if hotword_list_or_file is None:
self.hotword_list = None
# for text str input
elif not os.path.exists(hotword_list_or_file) and not hotword_list_or_file.startswith('http'):
logging.info("Attempting to parse hotwords as str...")
self.hotword_list = []
hotword_str_list = []
for hw in hotword_list_or_file.strip().split():
hotword_str_list.append(hw)
self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
self.hotword_list.append([self.asr_model.sos])
hotword_str_list.append('<s>')
logging.info("Hotword list: {}.".format(hotword_str_list))
# for local txt inputs
elif os.path.exists(hotword_list_or_file):
logging.info("Attempting to parse hotwords from local txt...")
self.hotword_list = []
hotword_str_list = []
with codecs.open(hotword_list_or_file, 'r') as fin:
for line in fin.readlines():
hw = line.strip()
hotword_str_list.append(hw)
self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
self.hotword_list.append([self.asr_model.sos])
hotword_str_list.append('<s>')
logging.info("Initialized hotword list from file: {}, hotword list: {}."
.format(hotword_list_or_file, hotword_str_list))
# for url, download and generate txt
else:
logging.info("Attempting to parse hotwords from url...")
work_dir = tempfile.TemporaryDirectory().name
if not os.path.exists(work_dir):
os.makedirs(work_dir)
text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
local_file = requests.get(hotword_list_or_file)
open(text_file_path, "wb").write(local_file.content)
hotword_list_or_file = text_file_path
self.hotword_list = []
hotword_str_list = []
with codecs.open(hotword_list_or_file, 'r') as fin:
for line in fin.readlines():
hw = line.strip()
hotword_str_list.append(hw)
self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
self.hotword_list.append([self.asr_model.sos])
hotword_str_list.append('<s>')
logging.info("Initialized hotword list from file: {}, hotword list: {}."
.format(hotword_list_or_file, hotword_str_list))
is_use_lm = lm_weight != 0.0 and lm_file is not None
if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
beam_search = None
self.beam_search = beam_search
logging.info(f"Beam_search: {self.beam_search}")
self.beam_search_transducer = beam_search_transducer
self.maxlenratio = maxlenratio
self.minlenratio = minlenratio
self.device = device
self.dtype = dtype
self.nbest = nbest
self.frontend = frontend
self.encoder_downsampling_factor = 1
if asr_train_args.encoder == "data2vec_encoder" or asr_train_args.encoder_conf["input_layer"] == "conv2d":
self.encoder_downsampling_factor = 4
@torch.no_grad()
def __call__(
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
):
"""Inference
Args:
speech: Input speech data
Returns:
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
if self.frontend is not None:
feats, feats_len = self.frontend.forward(speech, speech_lengths)
feats = to_device(feats, device=self.device)
feats_len = feats_len.int()
self.asr_model.frontend = None
else:
feats = speech
feats_len = speech_lengths
lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
batch = {"speech": feats, "speech_lengths": feats_len}
# a. To device
batch = to_device(batch, device=self.device)
# b. Forward Encoder
enc, enc_len = self.asr_model.encode(**batch)
if isinstance(enc, tuple):
enc = enc[0]
# assert len(enc) == 1, len(enc)
enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor
predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
predictor_outs[2], predictor_outs[3]
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
if not isinstance(self.asr_model, ContextualParaformer):
if self.hotword_list:
logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
else:
decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
results = []
b, n, d = decoder_out.size()
for i in range(b):
x = enc[i, :enc_len[i], :]
am_scores = decoder_out[i, :pre_token_length[i], :]
if self.beam_search is not None:
nbest_hyps = self.beam_search(
x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
)
nbest_hyps = nbest_hyps[: self.nbest]
else:
yseq = am_scores.argmax(dim=-1)
score = am_scores.max(dim=-1)[0]
score = torch.sum(score, dim=-1)
# pad with mask tokens to ensure compatibility with sos/eos tokens
yseq = torch.tensor(
[self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
)
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
for hyp in nbest_hyps:
assert isinstance(hyp, (Hypothesis)), type(hyp)
# remove sos/eos and get results
last_pos = -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 and x != 2, 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, enc_len_batch_total, lfr_factor))
# assert check_return_type(results)
return results
class Speech2TextExport:
"""Speech2TextExport class
"""
def __init__(
self,
asr_train_config: Union[Path, str] = None,
asr_model_file: Union[Path, str] = None,
cmvn_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,
ngram_weight: float = 0.9,
penalty: float = 0.0,
nbest: int = 1,
frontend_conf: dict = None,
hotword_list_or_file: str = None,
**kwargs,
):
# 1. Build ASR model
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, cmvn_file, device
)
frontend = None
if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
logging.info("asr_model: {}".format(asr_model))
logging.info("asr_train_args: {}".format(asr_train_args))
asr_model.to(dtype=getattr(torch, dtype)).eval()
token_list = asr_model.token_list
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.device = device
self.dtype = dtype
self.nbest = nbest
self.frontend = frontend
model = Paraformer_export(asr_model, onnx=False)
self.asr_model = model
@torch.no_grad()
def __call__(
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
):
"""Inference
Args:
speech: Input speech data
Returns:
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
if self.frontend is not None:
feats, feats_len = self.frontend.forward(speech, speech_lengths)
feats = to_device(feats, device=self.device)
feats_len = feats_len.int()
self.asr_model.frontend = None
else:
feats = speech
feats_len = speech_lengths
enc_len_batch_total = feats_len.sum()
lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
batch = {"speech": feats, "speech_lengths": feats_len}
# a. To device
batch = to_device(batch, device=self.device)
decoder_outs = self.asr_model(**batch)
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
results = []
b, n, d = decoder_out.size()
for i in range(b):
am_scores = decoder_out[i, :ys_pad_lens[i], :]
yseq = am_scores.argmax(dim=-1)
score = am_scores.max(dim=-1)[0]
score = torch.sum(score, dim=-1)
# pad with mask tokens to ensure compatibility with sos/eos tokens
yseq = torch.tensor(
yseq.tolist(), device=yseq.device
)
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
for hyp in nbest_hyps:
assert isinstance(hyp, (Hypothesis)), type(hyp)
# remove sos/eos and get results
last_pos = -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 and x != 2, 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, enc_len_batch_total, lfr_factor))
return results
def inference(
maxlenratio: float,
minlenratio: float,
batch_size: int,
beam_size: int,
ngpu: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
log_level: Union[int, str],
data_path_and_name_and_type,
asr_train_config: Optional[str],
asr_model_file: Optional[str],
cmvn_file: Optional[str] = None,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
lm_train_config: Optional[str] = None,
lm_file: Optional[str] = None,
token_type: Optional[str] = None,
key_file: Optional[str] = None,
word_lm_train_config: Optional[str] = None,
bpemodel: Optional[str] = None,
allow_variable_data_keys: bool = False,
streaming: bool = False,
output_dir: Optional[str] = None,
dtype: str = "float32",
seed: int = 0,
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
**kwargs,
):
inference_pipeline = inference_modelscope(
maxlenratio=maxlenratio,
minlenratio=minlenratio,
batch_size=batch_size,
beam_size=beam_size,
ngpu=ngpu,
ctc_weight=ctc_weight,
lm_weight=lm_weight,
penalty=penalty,
log_level=log_level,
asr_train_config=asr_train_config,
asr_model_file=asr_model_file,
cmvn_file=cmvn_file,
raw_inputs=raw_inputs,
lm_train_config=lm_train_config,
lm_file=lm_file,
token_type=token_type,
key_file=key_file,
word_lm_train_config=word_lm_train_config,
bpemodel=bpemodel,
allow_variable_data_keys=allow_variable_data_keys,
streaming=streaming,
output_dir=output_dir,
dtype=dtype,
seed=seed,
ngram_weight=ngram_weight,
nbest=nbest,
num_workers=num_workers,
**kwargs,
)
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
def inference_modelscope(
maxlenratio: float,
minlenratio: float,
batch_size: int,
beam_size: int,
ngpu: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
log_level: Union[int, str],
# data_path_and_name_and_type,
asr_train_config: Optional[str],
asr_model_file: Optional[str],
cmvn_file: Optional[str] = None,
lm_train_config: Optional[str] = None,
lm_file: Optional[str] = None,
token_type: Optional[str] = None,
key_file: Optional[str] = None,
word_lm_train_config: Optional[str] = None,
bpemodel: Optional[str] = None,
allow_variable_data_keys: bool = False,
dtype: str = "float32",
seed: int = 0,
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
output_dir: Optional[str] = None,
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
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",
)
export_mode = False
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
export_mode = param_dict.get("export_mode", False)
else:
hotword_list_or_file = None
if ngpu >= 1 and torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
batch_size = 1
# 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,
cmvn_file=cmvn_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,
ngram_weight=ngram_weight,
penalty=penalty,
nbest=nbest,
hotword_list_or_file=hotword_list_or_file,
)
if export_mode:
speech2text = Speech2TextExport(**speech2text_kwargs)
else:
speech2text = Speech2Text(**speech2text_kwargs)
def _forward(
data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
fs: dict = None,
param_dict: dict = None,
):
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:
if isinstance(raw_inputs, torch.Tensor):
raw_inputs = raw_inputs.numpy()
data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
fs=fs,
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,
)
forward_time_total = 0.0
length_total = 0.0
finish_count = 0
file_count = 1
# 7 .Start for-loop
# FIXME(kamo): The output format should be discussed about
asr_result_list = []
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
if output_path is not None:
writer = DatadirWriter(output_path)
else:
writer = None
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 for k, v in batch.items() if not k.endswith("_lengths")}
logging.info("decoding, utt_id: {}".format(keys))
# N-best list of (text, token, token_int, hyp_object)
time_beg = time.time()
results = speech2text(**batch)
if len(results) < 1:
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest
time_end = time.time()
forward_time = time_end - time_beg
lfr_factor = results[0][-1]
length = results[0][-2]
forward_time_total += forward_time
length_total += length
rtf_cur = "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".format(length, forward_time, 100 * forward_time / (length * lfr_factor))
logging.info(rtf_cur)
for batch_id in range(_bs):
result = [results[batch_id][:-2]]
key = keys[batch_id]
for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result):
# Create a directory: outdir/{n}best_recog
if writer is not None:
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)
ibest_writer["rtf"][key] = rtf_cur
if text is not None:
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
item = {'key': key, 'value': text_postprocessed}
asr_result_list.append(item)
finish_count += 1
# asr_utils.print_progress(finish_count / file_count)
if writer is not None:
ibest_writer["text"][key] = text_postprocessed
logging.info("decoding, utt: {}, predictions: {}".format(key, text))
rtf_avg = "decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor))
logging.info(rtf_avg)
if writer is not None:
ibest_writer["rtf"]["rtf_avf"] = rtf_avg
return asr_result_list
return _forward
def get_parser():
parser = config_argparse.ArgumentParser(
description="ASR 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",
)
parser.add_argument(
"--hotword",
type=str_or_none,
default=None,
help="hotword file path or hotwords seperated by space"
)
group = parser.add_argument_group("Input data related")
group.add_argument(
"--data_path_and_name_and_type",
type=str2triple_str,
required=False,
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(
"--asr_train_config",
type=str,
help="ASR training configuration",
)
group.add_argument(
"--asr_model_file",
type=str,
help="ASR model parameter file",
)
group.add_argument(
"--cmvn_file",
type=str,
help="Global cmvn file",
)
group.add_argument(
"--lm_train_config",
type=str,
help="LM training configuration",
)
group.add_argument(
"--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(
"--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(
"--model_tag",
type=str,
help="Pretrained model tag. If specify this option, *_train_config and "
"*_file will be overwritten",
)
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("--beam_size", type=int, default=20, help="Beam size")
group.add_argument("--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(
"--minlenratio",
type=float,
default=0.0,
help="Input length ratio to obtain min output length",
)
group.add_argument(
"--ctc_weight",
type=float,
default=0.5,
help="CTC weight in joint decoding",
)
group.add_argument("--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("--streaming", type=str2bool, default=False)
group.add_argument(
"--frontend_conf",
default=None,
help="",
)
group.add_argument("--raw_inputs", type=list, default=None)
# example=[{'key':'EdevDEWdIYQ_0021','file':'/mnt/data/jiangyu.xzy/test_data/speech_io/SPEECHIO_ASR_ZH00007_zhibodaihuo/wav/EdevDEWdIYQ_0021.wav'}])
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 ASR 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",
)
return parser
def main(cmd=None):
print(get_commandline_args(), file=sys.stderr)
parser = get_parser()
args = parser.parse_args(cmd)
param_dict = {'hotword': args.hotword}
kwargs = vars(args)
kwargs.pop("config", None)
kwargs['param_dict'] = param_dict
inference(**kwargs)
if __name__ == "__main__":
main()
# from modelscope.pipelines import pipeline
# from modelscope.utils.constant import Tasks
#
# inference_16k_pipline = pipeline(
# task=Tasks.auto_speech_recognition,
# model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
#
# rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
# print(rec_result)