mirror of
https://github.com/HumanAIGC/lite-avatar.git
synced 2026-02-05 01:49:19 +08:00
331 lines
12 KiB
Python
331 lines
12 KiB
Python
#coding=utf-8
|
|
import torch,os,sys,time,codecs,argparse,logging
|
|
from pathlib import Path
|
|
from typing import Optional,Tuple,Union,Dict,Any,List
|
|
import numpy as np
|
|
from typeguard import check_argument_types
|
|
from funasr_local.modules.beam_search.beam_search import BeamSearchPara as BeamSearch
|
|
from funasr_local.modules.beam_search.beam_search import Hypothesis
|
|
from funasr_local.modules.scorers.ctc import CTCPrefixScorer
|
|
from funasr_local.modules.scorers.length_bonus import LengthBonus
|
|
from funasr_local.tasks.asr import ASRTaskParaformer as ASRTask
|
|
from funasr_local.tasks.lm import LMTask
|
|
from funasr_local.text.build_tokenizer import build_tokenizer
|
|
from funasr_local.text.token_id_converter import TokenIDConverter
|
|
from funasr_local.torch_utils.device_funcs import to_device
|
|
from funasr_local.torch_utils.set_all_random_seed import set_all_random_seed
|
|
from funasr_local.utils import asr_utils, wav_utils, postprocess_utils
|
|
from funasr_local.models.frontend.wav_frontend import WavFrontend
|
|
import soundfile
|
|
import torch.nn.functional as F
|
|
import os
|
|
import time
|
|
global_asr_language: str = 'zh-cn'
|
|
global_sample_rate: Union[int, Dict[Any, int]] = {
|
|
'audio_fs': 16000,
|
|
'model_fs': 16000
|
|
}
|
|
|
|
def linear_interpolation(features, output_len=None):
|
|
features = features.transpose(1, 2)
|
|
output_features = F.interpolate(features,size=output_len,align_corners=True,mode='linear')
|
|
return output_features.transpose(1, 2)
|
|
|
|
class Speech2Text:
|
|
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,
|
|
**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=device
|
|
)
|
|
if asr_model.frontend is None and frontend_conf is not None:
|
|
frontend = WavFrontend(**frontend_conf)
|
|
asr_model.frontend = frontend
|
|
# logging.info("asr_model: {}".format(asr_model))
|
|
# logging.info("asr_train_args: {}".format(asr_train_args))
|
|
asr_model.to(dtype=getattr(torch, dtype)).eval()
|
|
|
|
# ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
|
|
# token_list = asr_model.token_list
|
|
# scorers.update(
|
|
# 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=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()
|
|
# # 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
|
|
# has_lm = lm_weight == 0.0 or lm_file is None
|
|
# if ctc_weight == 0.0 and has_lm:
|
|
# beam_search = None
|
|
# 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
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, frame_cnt = None
|
|
):
|
|
assert check_argument_types()
|
|
# Input as audio signal
|
|
if isinstance(speech, np.ndarray):
|
|
speech = torch.tensor(speech)
|
|
|
|
lfr_factor = max(1, (speech.size()[-1]//80)-1)
|
|
batch = {"speech": speech, "speech_lengths": speech_lengths}
|
|
# a. To device
|
|
batch = to_device(batch, device=self.device)
|
|
# b. Forward Encoder
|
|
enc_out, enc_len = self.asr_model.encode(**batch)
|
|
if isinstance(enc_out, tuple):
|
|
enc = enc_out[0]
|
|
hidden_states = enc_out[1]
|
|
# print(enc.size())
|
|
interp_enc = linear_interpolation(enc, frame_cnt)
|
|
# print(interp_enc.size())
|
|
|
|
|
|
|
|
interp_features = []
|
|
for hid in hidden_states:
|
|
interp_enc = linear_interpolation(hid, frame_cnt)
|
|
interp_features.append(interp_enc[0].cpu().numpy())
|
|
interp_features = np.asarray(interp_features).transpose(1,0,2)
|
|
# print(interp_features.shape)
|
|
'''
|
|
|
|
# assert len(enc) == 1, len(enc)
|
|
enc_len_batch_total = torch.sum(enc_len).item()
|
|
|
|
predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
|
|
pre_acoustic_embeds, pre_token_length = predictor_outs[0], predictor_outs[1]
|
|
pre_token_length = pre_token_length.round().long()
|
|
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]
|
|
|
|
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, 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 interp_features
|
|
|
|
model_path="./weights/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/" #模型路径
|
|
model_type='pytorch'
|
|
ngpu=0
|
|
log_level='ERROR'
|
|
|
|
data_path_and_name_and_type=['speech','sound',model_path+'am.mvn']
|
|
asr_model_file=model_path+'model.pb'
|
|
cmvn_file=model_path+'am.mvn'
|
|
idx_text=''
|
|
sampled_ids='seq2seq/sampled_ids'
|
|
sampled_lengths='seq2seq/sampled_lengths'
|
|
lang='zh-cn'
|
|
code_base='funasr_local'
|
|
mode='paraformer'
|
|
fs={'audio_fs=16000','model_fs=16000'}
|
|
beam_size=1
|
|
penalty=0.0
|
|
maxlenratio=0.0
|
|
minlenratio=0.0
|
|
ctc_weight=0.0
|
|
lm_weight=0.0
|
|
asr_train_config=model_path+'config.yaml'
|
|
lm_file=model_path+'lm/lm.pb'
|
|
lm_train_config=model_path+'lm/lm.yaml'
|
|
batch_size=1
|
|
frontend_conf={'fs':16000,'win_length':400,'hop_length':160,'window':'hamming','n_mels': 80, 'lfr_m': 7, 'lfr_n': 6}
|
|
token_num_relax=None
|
|
decoding_ind=None
|
|
decoding_mode=None
|
|
num_workers=0
|
|
device='cpu' #GPU设置'device':'cuda' CPU设置'device':'cpu'
|
|
# device='cpu'
|
|
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
|
|
dtype: str = "float32"
|
|
ngram_weight: float = 0.9
|
|
nbest: int = 1
|
|
fs: Union[dict, int] = 16000
|
|
|
|
hop_length: int = 160
|
|
sr = 16000
|
|
|
|
if isinstance(data_path_and_name_and_type[0], Tuple):
|
|
features_type: str = data_path_and_name_and_type[0][1]
|
|
elif isinstance(data_path_and_name_and_type[0], str):
|
|
features_type: str = data_path_and_name_and_type[1]
|
|
else:
|
|
raise NotImplementedError("unknown features type:{0}".format(data_path_and_name_and_type))
|
|
if features_type != 'sound':
|
|
frontend_conf = None
|
|
flag_modelscope = False
|
|
else:
|
|
flag_modelscope = True
|
|
if frontend_conf is not None:
|
|
if 'hop_length' in frontend_conf:
|
|
hop_length = frontend_conf['hop_length']
|
|
set_all_random_seed(0)
|
|
# 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,
|
|
frontend_conf=frontend_conf,
|
|
)
|
|
# print(speech2text_kwargs);input('')
|
|
speech2text = Speech2Text(**speech2text_kwargs)
|
|
# 3. Build data-iterator
|
|
|
|
def extract_para_feature(audio, frame_cnt):
|
|
s = time.time()
|
|
|
|
# results = speech2text(**batch)
|
|
batch_ = {"speech": torch.tensor(np.array([audio],dtype=np.float32)), 'frame_cnt': frame_cnt, "speech_lengths": torch.tensor(np.array([len(audio)]))}
|
|
# print('batch_',batch_ )#;input('')
|
|
results = speech2text(**batch_)
|
|
print('extract paraformer feature in {}ms'.format(round((time.time() - s),3)))
|
|
return results
|
|
|