mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
696 lines
23 KiB
Python
696 lines
23 KiB
Python
#!/usr/bin/env python3
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import argparse
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import logging
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import sys
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from pathlib import Path
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from typing import List
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Dict
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from typing import Any
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import numpy as np
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import torch
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from typeguard import check_argument_types
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from typeguard import check_return_type
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.modules.beam_search.beam_search import BeamSearchScama as BeamSearch
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from funasr.modules.beam_search.beam_search import Hypothesis
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from funasr.modules.scorers.ctc import CTCPrefixScorer
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from funasr.modules.scorers.length_bonus import LengthBonus
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from funasr.modules.subsampling import TooShortUttError
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from funasr.tasks.asr import ASRTaskUniASR as ASRTask
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from funasr.tasks.lm import LMTask
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from funasr.text.build_tokenizer import build_tokenizer
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from funasr.text.token_id_converter import TokenIDConverter
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils import asr_utils, wav_utils, postprocess_utils
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from funasr.models.frontend.wav_frontend import WavFrontend
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header_colors = '\033[95m'
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end_colors = '\033[0m'
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class Speech2Text:
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"""Speech2Text class
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Examples:
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>>> import soundfile
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>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
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>>> audio, rate = soundfile.read("speech.wav")
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>>> speech2text(audio)
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[(text, token, token_int, hypothesis object), ...]
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"""
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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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token_num_relax: int = 1,
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decoding_ind: int = 0,
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decoding_mode: str = "model1",
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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
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)
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frontend = None
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if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
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frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
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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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if decoding_mode == "model1":
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decoder = asr_model.decoder
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else:
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decoder = asr_model.decoder2
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if asr_model.ctc != None:
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ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
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scorers.update(
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ctc=ctc
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)
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token_list = asr_model.token_list
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scorers.update(
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decoder=decoder,
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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
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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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# logging.info(f"Beam_search: {beam_search}")
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logging.info(f"Decoding device={device}, dtype={dtype}")
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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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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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self.token_num_relax = token_num_relax
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self.decoding_ind = decoding_ind
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self.decoding_mode = decoding_mode
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self.frontend = frontend
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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
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) -> List[
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Tuple[
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Optional[str],
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List[str],
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List[int],
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Union[Hypothesis],
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]
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]:
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"""Inference
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Args:
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speech: Input speech data
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Returns:
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text, token, token_int, hyp
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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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if self.frontend is not None:
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feats, feats_len = self.frontend.forward(speech, speech_lengths)
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feats = to_device(feats, device=self.device)
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feats_len = feats_len.int()
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self.asr_model.frontend = None
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else:
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feats = speech
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feats_len = speech_lengths
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lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
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feats_raw = feats.clone().to(self.device)
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batch = {"speech": feats, "speech_lengths": feats_len}
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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, enc_len = self.asr_model.encode(**batch, ind=self.decoding_ind)
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if isinstance(enc, tuple):
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enc = enc[0]
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assert len(enc) == 1, len(enc)
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if self.decoding_mode == "model1":
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predictor_outs = self.asr_model.calc_predictor_mask(enc, enc_len)
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else:
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enc, enc_len = self.asr_model.encode2(enc, enc_len, feats_raw, feats_len, ind=self.decoding_ind)
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predictor_outs = self.asr_model.calc_predictor_mask2(enc, enc_len)
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scama_mask = predictor_outs[4]
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pre_token_length = predictor_outs[1]
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pre_acoustic_embeds = predictor_outs[0]
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maxlen = pre_token_length.sum().item() + self.token_num_relax
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minlen = max(0, pre_token_length.sum().item() - self.token_num_relax)
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# c. Passed the encoder result and the beam search
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nbest_hyps = self.beam_search(
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x=enc[0], scama_mask=scama_mask, pre_acoustic_embeds=pre_acoustic_embeds, maxlenratio=self.maxlenratio,
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minlenratio=self.minlenratio, maxlen=int(maxlen), minlen=int(minlen),
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)
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nbest_hyps = nbest_hyps[: self.nbest]
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results = []
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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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token = list(filter(lambda x: x != "<gbg>", token))
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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))
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assert check_return_type(results)
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return results
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def inference(
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maxlenratio: float,
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minlenratio: float,
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batch_size: int,
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beam_size: int,
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ngpu: int,
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ctc_weight: float,
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lm_weight: float,
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penalty: float,
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log_level: Union[int, str],
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data_path_and_name_and_type,
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asr_train_config: Optional[str],
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asr_model_file: Optional[str],
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ngram_file: Optional[str] = None,
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cmvn_file: Optional[str] = None,
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raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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lm_train_config: Optional[str] = None,
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lm_file: Optional[str] = None,
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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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output_dir: Optional[str] = None,
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dtype: str = "float32",
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seed: int = 0,
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ngram_weight: float = 0.9,
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nbest: int = 1,
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num_workers: int = 1,
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token_num_relax: int = 1,
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decoding_ind: int = 0,
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decoding_mode: str = "model1",
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**kwargs,
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):
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inference_pipeline = inference_modelscope(
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maxlenratio=maxlenratio,
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minlenratio=minlenratio,
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batch_size=batch_size,
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beam_size=beam_size,
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ngpu=ngpu,
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ctc_weight=ctc_weight,
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lm_weight=lm_weight,
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penalty=penalty,
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log_level=log_level,
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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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raw_inputs=raw_inputs,
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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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key_file=key_file,
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word_lm_train_config=word_lm_train_config,
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bpemodel=bpemodel,
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allow_variable_data_keys=allow_variable_data_keys,
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streaming=streaming,
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output_dir=output_dir,
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dtype=dtype,
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seed=seed,
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ngram_weight=ngram_weight,
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ngram_file=ngram_file,
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nbest=nbest,
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num_workers=num_workers,
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token_num_relax=token_num_relax,
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decoding_ind=decoding_ind,
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decoding_mode=decoding_mode,
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**kwargs,
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)
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return inference_pipeline(data_path_and_name_and_type, raw_inputs)
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def inference_modelscope(
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maxlenratio: float,
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minlenratio: float,
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batch_size: int,
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beam_size: int,
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ngpu: int,
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ctc_weight: float,
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lm_weight: float,
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penalty: float,
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log_level: Union[int, str],
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# data_path_and_name_and_type,
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asr_train_config: Optional[str],
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asr_model_file: Optional[str],
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ngram_file: Optional[str] = None,
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cmvn_file: Optional[str] = None,
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# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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lm_train_config: Optional[str] = None,
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lm_file: Optional[str] = None,
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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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output_dir: Optional[str] = None,
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dtype: str = "float32",
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seed: int = 0,
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ngram_weight: float = 0.9,
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nbest: int = 1,
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num_workers: int = 1,
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token_num_relax: int = 1,
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decoding_ind: int = 0,
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decoding_mode: str = "model1",
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param_dict: dict = None,
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**kwargs,
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):
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assert check_argument_types()
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if batch_size > 1:
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raise NotImplementedError("batch decoding is not implemented")
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if word_lm_train_config is not None:
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raise NotImplementedError("Word LM is not implemented")
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if ngpu > 1:
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raise NotImplementedError("only single GPU decoding is supported")
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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if param_dict is not None and "decoding_model" in param_dict:
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if param_dict["decoding_model"] == "fast":
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decoding_ind = 0
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decoding_mode = "model1"
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elif param_dict["decoding_model"] == "normal":
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decoding_ind = 0
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decoding_mode = "model2"
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elif param_dict["decoding_model"] == "offline":
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decoding_ind = 1
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decoding_mode = "model2"
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else:
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raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
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# 1. Set random-seed
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set_all_random_seed(seed)
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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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ngram_file=ngram_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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streaming=streaming,
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token_num_relax=token_num_relax,
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decoding_ind=decoding_ind,
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decoding_mode=decoding_mode,
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)
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speech2text = Speech2Text(**speech2text_kwargs)
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def _forward(data_path_and_name_and_type,
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raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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output_dir_v2: Optional[str] = None,
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fs: dict = None,
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param_dict: dict = None,
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**kwargs,
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):
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# 3. Build data-iterator
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if data_path_and_name_and_type is None and raw_inputs is not None:
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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loader = ASRTask.build_streaming_iterator(
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data_path_and_name_and_type,
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dtype=dtype,
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fs=fs,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
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collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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finish_count = 0
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file_count = 1
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# 7 .Start for-loop
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# FIXME(kamo): The output format should be discussed about
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asr_result_list = []
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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if output_path is not None:
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writer = DatadirWriter(output_path)
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else:
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writer = None
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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)
|
|
except TooShortUttError as e:
|
|
logging.warning(f"Utterance {keys} {e}")
|
|
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
|
|
results = [[" ", ["sil"], [2], hyp]] * nbest
|
|
|
|
# Only supporting batch_size==1
|
|
key = keys[0]
|
|
logging.info(f"Utterance: {key}")
|
|
for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
|
|
# 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)
|
|
|
|
if text is not None:
|
|
text_postprocessed, word_lists = 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] = " ".join(word_lists)
|
|
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",
|
|
)
|
|
|
|
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("--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.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 = 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",
|
|
)
|
|
group.add_argument("--token_num_relax", type=int, default=1, help="")
|
|
group.add_argument("--decoding_ind", type=int, default=0, help="")
|
|
group.add_argument("--decoding_mode", type=str, default="model1", help="")
|
|
group.add_argument(
|
|
"--ctc_weight2",
|
|
type=float,
|
|
default=0.0,
|
|
help="CTC weight in joint decoding",
|
|
)
|
|
return parser
|
|
|
|
|
|
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()
|