mirror of
https://github.com/HumanAIGC/lite-avatar.git
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331 lines
12 KiB
Python
331 lines
12 KiB
Python
#coding=utf-8
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import torch,os,sys,time,codecs,argparse,logging
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from pathlib import Path
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from typing import Optional,Tuple,Union,Dict,Any,List
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import numpy as np
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from typeguard import check_argument_types
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from funasr_local.modules.beam_search.beam_search import BeamSearchPara as BeamSearch
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from funasr_local.modules.beam_search.beam_search import Hypothesis
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from funasr_local.modules.scorers.ctc import CTCPrefixScorer
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from funasr_local.modules.scorers.length_bonus import LengthBonus
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from funasr_local.tasks.asr import ASRTaskParaformer as ASRTask
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from funasr_local.tasks.lm import LMTask
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from funasr_local.text.build_tokenizer import build_tokenizer
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from funasr_local.text.token_id_converter import TokenIDConverter
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from funasr_local.torch_utils.device_funcs import to_device
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from funasr_local.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr_local.utils import asr_utils, wav_utils, postprocess_utils
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from funasr_local.models.frontend.wav_frontend import WavFrontend
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import soundfile
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import torch.nn.functional as F
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import os
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import time
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global_asr_language: str = 'zh-cn'
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global_sample_rate: Union[int, Dict[Any, int]] = {
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'audio_fs': 16000,
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'model_fs': 16000
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}
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def linear_interpolation(features, output_len=None):
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features = features.transpose(1, 2)
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output_features = F.interpolate(features,size=output_len,align_corners=True,mode='linear')
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return output_features.transpose(1, 2)
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class Speech2Text:
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def __init__(
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self,
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asr_train_config: Union[Path, str] = None,
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asr_model_file: Union[Path, str] = None,
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cmvn_file: Union[Path, str] = None,
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lm_train_config: Union[Path, str] = None,
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lm_file: Union[Path, str] = None,
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token_type: str = None,
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bpemodel: str = None,
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device: str = "cpu",
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maxlenratio: float = 0.0,
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minlenratio: float = 0.0,
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dtype: str = "float32",
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beam_size: int = 20,
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ctc_weight: float = 0.5,
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lm_weight: float = 1.0,
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ngram_weight: float = 0.9,
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penalty: float = 0.0,
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nbest: int = 1,
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frontend_conf: dict = None,
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**kwargs,
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):
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assert check_argument_types()
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# 1. Build ASR model
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scorers = {}
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asr_model, asr_train_args = ASRTask.build_model_from_file(
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asr_train_config, asr_model_file, cmvn_file, device=device
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)
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if asr_model.frontend is None and frontend_conf is not None:
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frontend = WavFrontend(**frontend_conf)
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asr_model.frontend = frontend
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# logging.info("asr_model: {}".format(asr_model))
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# logging.info("asr_train_args: {}".format(asr_train_args))
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asr_model.to(dtype=getattr(torch, dtype)).eval()
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# ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
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# token_list = asr_model.token_list
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# scorers.update(
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# ctc=ctc,
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# length_bonus=LengthBonus(len(token_list)),
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# )
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# # 2. Build Language model
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# if lm_train_config is not None:
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# lm, lm_train_args = LMTask.build_model_from_file(
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# lm_train_config, lm_file, device=device
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# )
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# scorers["lm"] = lm.lm
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# # 3. Build ngram model
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# # ngram is not supported now
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# ngram = None
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# scorers["ngram"] = ngram
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# # 4. Build BeamSearch object
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# # transducer is not supported now
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# beam_search_transducer = None
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# weights = dict(
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# decoder=1.0 - ctc_weight,
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# ctc=ctc_weight,
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# lm=lm_weight,
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# ngram=ngram_weight,
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# length_bonus=penalty,
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# )
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# beam_search = BeamSearch(
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# beam_size=beam_size,
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# weights=weights,
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# scorers=scorers,
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# sos=asr_model.sos,
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# eos=asr_model.eos,
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# vocab_size=len(token_list),
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# token_list=token_list,
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# pre_beam_score_key=None if ctc_weight == 1.0 else "full",
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# )
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# beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
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# for scorer in scorers.values():
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# if isinstance(scorer, torch.nn.Module):
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# scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
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# # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
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# if token_type is None:
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# token_type = asr_train_args.token_type
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# if bpemodel is None:
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# bpemodel = asr_train_args.bpemodel
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# if token_type is None:
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# tokenizer = None
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# elif token_type == "bpe":
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# if bpemodel is not None:
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# tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
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# else:
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# tokenizer = None
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# else:
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# tokenizer = build_tokenizer(token_type=token_type)
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# converter = TokenIDConverter(token_list=token_list)
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# # logging.info(f"Text tokenizer: {tokenizer}")
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self.asr_model = asr_model
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# self.asr_train_args = asr_train_args
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# self.converter = converter
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# self.tokenizer = tokenizer
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# has_lm = lm_weight == 0.0 or lm_file is None
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# if ctc_weight == 0.0 and has_lm:
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# beam_search = None
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# self.beam_search = beam_search
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# self.beam_search_transducer = beam_search_transducer
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# self.maxlenratio = maxlenratio
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# self.minlenratio = minlenratio
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self.device = device
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# self.dtype = dtype
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# self.nbest = nbest
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@torch.no_grad()
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def __call__(
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self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, frame_cnt = None
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):
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assert check_argument_types()
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# Input as audio signal
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if isinstance(speech, np.ndarray):
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speech = torch.tensor(speech)
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lfr_factor = max(1, (speech.size()[-1]//80)-1)
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batch = {"speech": speech, "speech_lengths": speech_lengths}
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# a. To device
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batch = to_device(batch, device=self.device)
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# b. Forward Encoder
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enc_out, enc_len = self.asr_model.encode(**batch)
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if isinstance(enc_out, tuple):
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enc = enc_out[0]
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hidden_states = enc_out[1]
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# print(enc.size())
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interp_enc = linear_interpolation(enc, frame_cnt)
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# print(interp_enc.size())
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interp_features = []
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for hid in hidden_states:
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interp_enc = linear_interpolation(hid, frame_cnt)
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interp_features.append(interp_enc[0].cpu().numpy())
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interp_features = np.asarray(interp_features).transpose(1,0,2)
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# print(interp_features.shape)
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'''
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# assert len(enc) == 1, len(enc)
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enc_len_batch_total = torch.sum(enc_len).item()
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predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
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pre_acoustic_embeds, pre_token_length = predictor_outs[0], predictor_outs[1]
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pre_token_length = pre_token_length.round().long()
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decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
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decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
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results = []
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b, n, d = decoder_out.size()
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for i in range(b):
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x = enc[i, :enc_len[i], :]
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am_scores = decoder_out[i, :pre_token_length[i], :]
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if self.beam_search is not None:
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nbest_hyps = self.beam_search(
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x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
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)
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nbest_hyps = nbest_hyps[: self.nbest]
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else:
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yseq = am_scores.argmax(dim=-1)
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score = am_scores.max(dim=-1)[0]
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score = torch.sum(score, dim=-1)
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# pad with mask tokens to ensure compatibility with sos/eos tokens
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yseq = torch.tensor(
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[self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
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)
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nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
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for hyp in nbest_hyps:
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assert isinstance(hyp, (Hypothesis)), type(hyp)
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# remove sos/eos and get results
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last_pos = -1
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if isinstance(hyp.yseq, list):
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token_int = hyp.yseq[1:last_pos]
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else:
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token_int = hyp.yseq[1:last_pos].tolist()
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# remove blank symbol id, which is assumed to be 0
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token_int = list(filter(lambda x: x != 0, token_int))
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# Change integer-ids to tokens
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token = self.converter.ids2tokens(token_int)
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if self.tokenizer is not None:
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text = self.tokenizer.tokens2text(token)
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else:
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text = None
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results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
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# assert check_return_type(results)
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'''
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return interp_features
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model_path="./weights/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/" #模型路径
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model_type='pytorch'
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ngpu=0
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log_level='ERROR'
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data_path_and_name_and_type=['speech','sound',model_path+'am.mvn']
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asr_model_file=model_path+'model.pb'
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cmvn_file=model_path+'am.mvn'
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idx_text=''
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sampled_ids='seq2seq/sampled_ids'
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sampled_lengths='seq2seq/sampled_lengths'
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lang='zh-cn'
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code_base='funasr_local'
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mode='paraformer'
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fs={'audio_fs=16000','model_fs=16000'}
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beam_size=1
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penalty=0.0
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maxlenratio=0.0
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minlenratio=0.0
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ctc_weight=0.0
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lm_weight=0.0
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asr_train_config=model_path+'config.yaml'
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lm_file=model_path+'lm/lm.pb'
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lm_train_config=model_path+'lm/lm.yaml'
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batch_size=1
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frontend_conf={'fs':16000,'win_length':400,'hop_length':160,'window':'hamming','n_mels': 80, 'lfr_m': 7, 'lfr_n': 6}
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token_num_relax=None
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decoding_ind=None
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decoding_mode=None
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num_workers=0
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device='cpu' #GPU设置'device':'cuda' CPU设置'device':'cpu'
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# device='cpu'
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token_type: Optional[str] = None
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key_file: Optional[str] = None
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word_lm_train_config: Optional[str] = None
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bpemodel: Optional[str] = None
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allow_variable_data_keys: bool = False
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streaming: bool = False
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dtype: str = "float32"
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ngram_weight: float = 0.9
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nbest: int = 1
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fs: Union[dict, int] = 16000
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hop_length: int = 160
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sr = 16000
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if isinstance(data_path_and_name_and_type[0], Tuple):
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features_type: str = data_path_and_name_and_type[0][1]
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elif isinstance(data_path_and_name_and_type[0], str):
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features_type: str = data_path_and_name_and_type[1]
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else:
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raise NotImplementedError("unknown features type:{0}".format(data_path_and_name_and_type))
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if features_type != 'sound':
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frontend_conf = None
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flag_modelscope = False
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else:
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flag_modelscope = True
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if frontend_conf is not None:
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if 'hop_length' in frontend_conf:
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hop_length = frontend_conf['hop_length']
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set_all_random_seed(0)
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# 2. Build speech2text
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speech2text_kwargs = dict(
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asr_train_config=asr_train_config,
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asr_model_file=asr_model_file,
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cmvn_file=cmvn_file,
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lm_train_config=lm_train_config,
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lm_file=lm_file,
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token_type=token_type,
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bpemodel=bpemodel,
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device=device,
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maxlenratio=maxlenratio,
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minlenratio=minlenratio,
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dtype=dtype,
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beam_size=beam_size,
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ctc_weight=ctc_weight,
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lm_weight=lm_weight,
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ngram_weight=ngram_weight,
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penalty=penalty,
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nbest=nbest,
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frontend_conf=frontend_conf,
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)
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# print(speech2text_kwargs);input('')
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speech2text = Speech2Text(**speech2text_kwargs)
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# 3. Build data-iterator
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def extract_para_feature(audio, frame_cnt):
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s = time.time()
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# results = speech2text(**batch)
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batch_ = {"speech": torch.tensor(np.array([audio],dtype=np.float32)), 'frame_cnt': frame_cnt, "speech_lengths": torch.tensor(np.array([len(audio)]))}
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# print('batch_',batch_ )#;input('')
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results = speech2text(**batch_)
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print('extract paraformer feature in {}ms'.format(round((time.time() - s),3)))
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return results
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