mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
650 lines
22 KiB
Python
Executable File
650 lines
22 KiB
Python
Executable File
#!/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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import time
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from pathlib import Path
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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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from typing import List
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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 funasr.fileio.datadir_writer import DatadirWriter
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from funasr.modules.beam_search.beam_search import BeamSearchPara 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 ASRTaskParaformer 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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from modelscope.utils.logger import get_logger
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logger = get_logger()
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header_colors = '\033[95m'
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end_colors = '\033[0m'
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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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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.pth")
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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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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, 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
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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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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
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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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# data: (Nsamples,) -> (1, Nsamples)
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# lengths: (1,)
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# if len(speech.size()) < 3:
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# speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
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# speech_lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
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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, enc_len = self.asr_model.encode(**batch)
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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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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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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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audio_lists: Union[List[Any], bytes] = 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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frontend_conf: dict = None,
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fs: Union[dict, int] = 16000,
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lang: Optional[str] = None,
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**kwargs,
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):
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assert check_argument_types()
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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:
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device = "cuda"
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else:
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device = "cpu"
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hop_length: int = 160
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sr: int = 16000
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if isinstance(fs, int):
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sr = fs
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else:
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if 'model_fs' in fs and fs['model_fs'] is not None:
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sr = fs['model_fs']
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# data_path_and_name_and_type for modelscope: (data from audio_lists)
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# ['speech', 'sound', 'am.mvn']
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# data_path_and_name_and_type for funasr:
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# [('/mnt/data/jiangyu.xzy/exp/maas/mvn.1.scp', 'speech', 'kaldi_ark')]
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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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finish_count = 0
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file_count = 1
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if flag_modelscope and not isinstance(data_path_and_name_and_type[0], Tuple):
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data_path_and_name_and_type_new = [
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audio_lists, data_path_and_name_and_type[0], data_path_and_name_and_type[1]
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]
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if isinstance(audio_lists, bytes):
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file_count = 1
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else:
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file_count = len(audio_lists)
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if len(data_path_and_name_and_type) >= 3 and frontend_conf is not None:
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mvn_file = data_path_and_name_and_type[2]
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mvn_data = wav_utils.extract_CMVN_featrures(mvn_file)
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frontend_conf['mvn_data'] = mvn_data
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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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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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speech2text = Speech2Text(**speech2text_kwargs)
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# 3. Build data-iterator
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if flag_modelscope:
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loader = ASRTask.build_streaming_iterator_modelscope(
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data_path_and_name_and_type_new,
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dtype=dtype,
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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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sample_rate=fs
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)
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else:
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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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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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forward_time_total = 0.0
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length_total = 0.0
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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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if output_dir is not None:
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writer = DatadirWriter(output_dir)
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else:
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writer = None
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for keys, batch in loader:
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assert isinstance(batch, dict), type(batch)
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assert all(isinstance(s, str) for s in keys), keys
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_bs = len(next(iter(batch.values())))
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assert len(keys) == _bs, f"{len(keys)} != {_bs}"
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# batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
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# logging.info("decoding, utt_id: {}".format(keys))
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# N-best list of (text, token, token_int, hyp_object)
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time_beg = time.time()
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results = speech2text(**batch)
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time_end = time.time()
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forward_time = time_end - time_beg
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lfr_factor = results[0][-1]
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length = results[0][-2]
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forward_time_total += forward_time
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length_total += length
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logging.info(
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"decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
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format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
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for batch_id in range(_bs):
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result = [results[batch_id][:-2]]
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key = keys[batch_id]
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for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result):
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# Create a directory: outdir/{n}best_recog
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if writer is not None:
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ibest_writer = writer[f"{n}best_recog"]
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# Write the result to each file
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ibest_writer["token"][key] = " ".join(token)
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ibest_writer["token_int"][key] = " ".join(map(str, token_int))
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ibest_writer["score"][key] = str(hyp.score)
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if text is not None:
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text_postprocessed = postprocess_utils.sentence_postprocess(token)
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item = {'key': key, 'value': text_postprocessed}
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asr_result_list.append(item)
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finish_count += 1
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asr_utils.print_progress(finish_count / file_count)
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if writer is not None:
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ibest_writer["text"][key] = text
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logging.info("decoding, utt: {}, predictions: {}".format(key, text))
|
|
|
|
logging.info("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)))
|
|
return asr_result_list
|
|
|
|
|
|
def set_parameters(language: str = None,
|
|
sample_rate: Union[int, Dict[Any, int]] = None):
|
|
if language is not None:
|
|
global global_asr_language
|
|
global_asr_language = language
|
|
if sample_rate is not None:
|
|
global global_sample_rate
|
|
global_sample_rate = sample_rate
|
|
|
|
|
|
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=True,
|
|
action="append",
|
|
)
|
|
group.add_argument("--key_file", type=str_or_none)
|
|
group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
|
|
|
|
group = parser.add_argument_group("The model configuration related")
|
|
group.add_argument(
|
|
"--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(
|
|
"--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("--audio_lists", 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)
|
|
kwargs = vars(args)
|
|
kwargs.pop("config", None)
|
|
inference(**kwargs)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|