From 2c3f49cd21a1da317b028878b27362c8af1ce8b5 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 20 二月 2023 16:08:23 +0800
Subject: [PATCH] readme
---
/dev/null | 632 ---------------------------------------------------------
1 files changed, 0 insertions(+), 632 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer_timestamp.py b/funasr/bin/asr_inference_paraformer_timestamp.py
deleted file mode 100644
index d0baa74..0000000
--- a/funasr/bin/asr_inference_paraformer_timestamp.py
+++ /dev/null
@@ -1,632 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import logging
-import sys
-import time
-from pathlib import Path
-from typing import Optional
-from typing import Sequence
-from typing import Tuple
-from typing import Union
-from typing import Dict
-from typing import Any
-from typing import List
-from unittest import result
-
-import numpy as np
-import torch
-from typeguard import check_argument_types
-
-from funasr.fileio.datadir_writer import DatadirWriter
-from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch
-from funasr.modules.beam_search.beam_search import Hypothesis
-from funasr.modules.scorers.ctc import CTCPrefixScorer
-from funasr.modules.scorers.length_bonus import LengthBonus
-from funasr.modules.subsampling import TooShortUttError
-from funasr.tasks.asr import ASRTaskParaformer as ASRTask
-from funasr.tasks.lm import LMTask
-from funasr.text.build_tokenizer import build_tokenizer
-from funasr.text.token_id_converter import TokenIDConverter
-from funasr.torch_utils.device_funcs import to_device
-from funasr.torch_utils.set_all_random_seed import set_all_random_seed
-from funasr.utils import config_argparse
-from funasr.utils.cli_utils import get_commandline_args
-from funasr.utils.types import str2bool
-from funasr.utils.types import str2triple_str
-from funasr.utils.types import str_or_none
-from funasr.utils import asr_utils, wav_utils, postprocess_utils
-from funasr.models.frontend.wav_frontend import WavFrontend
-from funasr.models.predictor.cif import CifPredictorV3
-from funasr.utils.timestamp_tools import time_stamp_lfr6_advance
-
-header_colors = '\033[95m'
-end_colors = '\033[0m'
-
-global_asr_language: str = 'zh-cn'
-global_sample_rate: Union[int, Dict[Any, int]] = {
- 'audio_fs': 16000,
- 'model_fs': 16000
-}
-
-class Speech2Text:
- """Speech2Text class
-
- Examples:
- >>> import soundfile
- >>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
- >>> audio, rate = soundfile.read("speech.wav")
- >>> speech2text(audio)
- [(text, token, token_int, hypothesis object), ...]
-
- """
-
- def __init__(
- self,
- asr_train_config: Union[Path, str] = None,
- asr_model_file: Union[Path, str] = None,
- cmvn_file: Union[Path, str] = None,
- lm_train_config: Union[Path, str] = None,
- lm_file: Union[Path, str] = None,
- token_type: str = None,
- bpemodel: str = None,
- device: str = "cpu",
- maxlenratio: float = 0.0,
- minlenratio: float = 0.0,
- dtype: str = "float32",
- beam_size: int = 20,
- ctc_weight: float = 0.5,
- lm_weight: float = 1.0,
- ngram_weight: float = 0.9,
- penalty: float = 0.0,
- nbest: int = 1,
- frontend_conf: dict = None,
- time_stamp_writer: bool = False,
- **kwargs,
- ):
- assert check_argument_types()
-
- # 1. Build ASR model
- scorers = {}
- asr_model, asr_train_args = ASRTask.build_model_from_file(
- asr_train_config, asr_model_file, cmvn_file, device
- )
- frontend = None
- if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
- frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
-
- logging.info("asr_model: {}".format(asr_model))
- logging.info("asr_train_args: {}".format(asr_train_args))
- asr_model.to(dtype=getattr(torch, dtype)).eval()
-
- if asr_model.ctc != None:
- ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
- scorers.update(
- ctc=ctc
- )
- token_list = asr_model.token_list
- scorers.update(
- length_bonus=LengthBonus(len(token_list)),
- )
-
- # 2. Build Language model
- if lm_train_config is not None:
- lm, lm_train_args = LMTask.build_model_from_file(
- lm_train_config, lm_file, device
- )
- scorers["lm"] = lm.lm
-
- # 3. Build ngram model
- # ngram is not supported now
- ngram = None
- scorers["ngram"] = ngram
-
- # 4. Build BeamSearch object
- # transducer is not supported now
- beam_search_transducer = None
-
- weights = dict(
- decoder=1.0 - ctc_weight,
- ctc=ctc_weight,
- lm=lm_weight,
- ngram=ngram_weight,
- length_bonus=penalty,
- )
- beam_search = BeamSearch(
- beam_size=beam_size,
- weights=weights,
- scorers=scorers,
- sos=asr_model.sos,
- eos=asr_model.eos,
- vocab_size=len(token_list),
- token_list=token_list,
- pre_beam_score_key=None if ctc_weight == 1.0 else "full",
- )
-
- beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
- for scorer in scorers.values():
- if isinstance(scorer, torch.nn.Module):
- scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
-
- logging.info(f"Decoding device={device}, dtype={dtype}")
-
- # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
- if token_type is None:
- token_type = asr_train_args.token_type
- if bpemodel is None:
- bpemodel = asr_train_args.bpemodel
-
- if token_type is None:
- tokenizer = None
- elif token_type == "bpe":
- if bpemodel is not None:
- tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
- else:
- tokenizer = None
- else:
- tokenizer = build_tokenizer(token_type=token_type)
- converter = TokenIDConverter(token_list=token_list)
- logging.info(f"Text tokenizer: {tokenizer}")
-
- self.asr_model = asr_model
- self.asr_train_args = asr_train_args
- self.converter = converter
- self.tokenizer = tokenizer
- is_use_lm = lm_weight != 0.0 and lm_file is not None
- if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
- beam_search = None
- self.beam_search = beam_search
- logging.info(f"Beam_search: {self.beam_search}")
- self.beam_search_transducer = beam_search_transducer
- self.maxlenratio = maxlenratio
- self.minlenratio = minlenratio
- self.device = device
- self.dtype = dtype
- self.nbest = nbest
- self.frontend = frontend
- self.encoder_downsampling_factor = 1
- if asr_train_args.encoder_conf["input_layer"] == "conv2d":
- self.encoder_downsampling_factor = 4
- self.time_stamp_writer = time_stamp_writer
-
-
- @torch.no_grad()
- def __call__(
- self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
- ):
- """Inference
-
- Args:
- speech: Input speech data
- Returns:
- text, token, token_int, hyp
-
- """
- assert check_argument_types()
-
- # Input as audio signal
- if isinstance(speech, np.ndarray):
- speech = torch.tensor(speech)
-
- if self.frontend is not None:
- feats, feats_len = self.frontend.forward(speech, speech_lengths)
- feats = to_device(feats, device=self.device)
- feats_len = feats_len.int()
- self.asr_model.frontend = None
- else:
- feats = speech
- feats_len = speech_lengths
- lfr_factor = max(1, (feats.size()[-1]//80)-1)
- batch = {"speech": feats, "speech_lengths": feats_len}
-
- # a. To device
- batch = to_device(batch, device=self.device)
-
- # b. Forward Encoder
- enc, enc_len = self.asr_model.encode(**batch)
- if isinstance(enc, tuple):
- enc = enc[0]
- # assert len(enc) == 1, len(enc)
- enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor
-
- predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
- pre_acoustic_embeds, pre_token_length, _, _ = predictor_outs[0], predictor_outs[1], predictor_outs[2], predictor_outs[3]
- pre_token_length = pre_token_length.round().long()
-
- if isinstance(self.asr_model.predictor, CifPredictorV3) and self.time_stamp_writer:
- ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len, pre_token_length)
- timestamp = (ds_alphas, ds_cif_peak, us_alphas, us_cif_peak)
- else:
- timestamp = None
-
- decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
- decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
-
- results = []
- b, n, d = decoder_out.size()
- for i in range(b):
- x = enc[i, :enc_len[i], :]
- am_scores = decoder_out[i, :pre_token_length[i], :]
- if self.beam_search is not None:
- nbest_hyps = self.beam_search(
- x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
- )
-
- nbest_hyps = nbest_hyps[: self.nbest]
- else:
- yseq = am_scores.argmax(dim=-1)
- score = am_scores.max(dim=-1)[0]
- score = torch.sum(score, dim=-1)
- # pad with mask tokens to ensure compatibility with sos/eos tokens
- yseq = torch.tensor(
- [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
- )
- nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
-
- for hyp in nbest_hyps:
- assert isinstance(hyp, (Hypothesis)), type(hyp)
-
- # remove sos/eos and get results
- last_pos = -1
- if isinstance(hyp.yseq, list):
- token_int = hyp.yseq[1:last_pos]
- else:
- token_int = hyp.yseq[1:last_pos].tolist()
-
- # remove blank symbol id, which is assumed to be 0
- token_int = list(filter(lambda x: x != 0, token_int))
-
- # Change integer-ids to tokens
- token = self.converter.ids2tokens(token_int)
-
- if self.tokenizer is not None:
- text = self.tokenizer.tokens2text(token)
- else:
- text = None
-
- if timestamp is not None:
- tst = [_tst[i] for _tst in timestamp] # timestamp related tensors
- results.append((text, token, token_int, hyp, tst, enc_len_batch_total, lfr_factor))
- else:
- results.append((text, token, token_int, hyp, None, enc_len_batch_total, lfr_factor))
-
- return results
-
-
-def inference(
- maxlenratio: float,
- minlenratio: float,
- batch_size: int,
- beam_size: int,
- ngpu: int,
- ctc_weight: float,
- lm_weight: float,
- penalty: float,
- log_level: Union[int, str],
- data_path_and_name_and_type,
- asr_train_config: Optional[str],
- asr_model_file: Optional[str],
- cmvn_file: Optional[str] = None,
- audio_lists: Union[List[Any], bytes] = None,
- lm_train_config: Optional[str] = None,
- lm_file: Optional[str] = None,
- token_type: Optional[str] = None,
- key_file: Optional[str] = None,
- word_lm_train_config: Optional[str] = None,
- bpemodel: Optional[str] = None,
- allow_variable_data_keys: bool = False,
- streaming: bool = False,
- output_dir: Optional[str] = None,
- dtype: str = "float32",
- seed: int = 0,
- ngram_weight: float = 0.9,
- nbest: int = 1,
- num_workers: int = 1,
- frontend_conf: dict = None,
- fs: Union[dict, int] = 16000,
- lang: Optional[str] = None,
- time_stamp_writer: bool = False,
- **kwargs,
-):
- assert check_argument_types()
-
- if word_lm_train_config is not None:
- raise NotImplementedError("Word LM is not implemented")
- if ngpu > 1:
- raise NotImplementedError("only single GPU decoding is supported")
-
- logging.basicConfig(
- level=log_level,
- format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
- )
-
- if ngpu >= 1 and torch.cuda.is_available():
- device = "cuda"
- else:
- device = "cpu"
-
- # 1. Set random-seed
- set_all_random_seed(seed)
-
- # 2. Build speech2text
- speech2text_kwargs = dict(
- asr_train_config=asr_train_config,
- asr_model_file=asr_model_file,
- cmvn_file=cmvn_file,
- lm_train_config=lm_train_config,
- lm_file=lm_file,
- token_type=token_type,
- bpemodel=bpemodel,
- device=device,
- maxlenratio=maxlenratio,
- minlenratio=minlenratio,
- dtype=dtype,
- beam_size=beam_size,
- ctc_weight=ctc_weight,
- lm_weight=lm_weight,
- ngram_weight=ngram_weight,
- penalty=penalty,
- nbest=nbest,
- frontend_conf=frontend_conf,
- time_stamp_writer=time_stamp_writer,
- )
- speech2text = Speech2Text(**speech2text_kwargs)
-
- # 3. Build data-iterator
- loader = ASRTask.build_streaming_iterator(
- data_path_and_name_and_type,
- dtype=dtype,
- batch_size=batch_size,
- key_file=key_file,
- num_workers=num_workers,
- preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
- collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
- allow_variable_data_keys=allow_variable_data_keys,
- inference=True,
- )
-
- forward_time_total = 0.0
- length_total = 0.0
- finish_count = 0
- file_count = 1
- # 7 .Start for-loop
- # FIXME(kamo): The output format should be discussed about
- asr_result_list = []
- if output_dir is not None:
- writer = DatadirWriter(output_dir)
- else:
- writer = None
-
- for keys, batch in loader:
- assert isinstance(batch, dict), type(batch)
- assert all(isinstance(s, str) for s in keys), keys
- _bs = len(next(iter(batch.values())))
- assert len(keys) == _bs, f"{len(keys)} != {_bs}"
- # batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
-
- logging.info("decoding, utt_id: {}".format(keys))
- # N-best list of (text, token, token_int, hyp_object)
-
- time_beg = time.time()
- results = speech2text(**batch)
- if len(results) < 1:
- hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
- results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest
- time_end = time.time()
- forward_time = time_end - time_beg
- lfr_factor = results[0][-1]
- length = results[0][-2]
- forward_time_total += forward_time
- length_total += length
- logging.info(
- "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
- format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
-
- for batch_id in range(_bs):
- result = [results[batch_id][:5]]
-
- key = keys[batch_id]
- for n, (text, token, token_int, hyp, tst) in zip(range(1, nbest + 1), result):
- # Create a directory: outdir/{n}best_recog
- if writer is not None:
- ibest_writer = writer[f"{n}best_recog"]
-
- # Write the result to each file
- ibest_writer["token"][key] = " ".join(token)
- ibest_writer["token_int"][key] = " ".join(map(str, token_int))
- ibest_writer["score"][key] = str(hyp.score)
-
- if text is not None:
- text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
- item = {'key': key, 'value': text_postprocessed}
- asr_result_list.append(item)
- finish_count += 1
- # asr_utils.print_progress(finish_count / file_count)
- if writer is not None:
- ibest_writer["text"][key] = text
-
- if tst is not None:
- timestamp_res = time_stamp_lfr6_advance(tst, text)
-
-
- 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 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(
- "--cmvn_file",
- type=str,
- help="Global cmvn file",
- )
- group.add_argument(
- "--lm_train_config",
- type=str,
- help="LM training configuration",
- )
- group.add_argument(
- "--lm_file",
- type=str,
- help="LM parameter file",
- )
- group.add_argument(
- "--word_lm_train_config",
- type=str,
- help="Word LM training configuration",
- )
- group.add_argument(
- "--word_lm_file",
- type=str,
- help="Word LM parameter file",
- )
- group.add_argument(
- "--ngram_file",
- type=str,
- help="N-gram parameter file",
- )
- group.add_argument(
- "--model_tag",
- type=str,
- help="Pretrained model tag. If specify this option, *_train_config and "
- "*_file will be overwritten",
- )
-
- group = parser.add_argument_group("Beam-search related")
- group.add_argument(
- "--batch_size",
- type=int,
- default=1,
- help="The batch size for inference",
- )
- group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
- group.add_argument("--beam_size", type=int, default=20, help="Beam size")
- group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
- group.add_argument(
- "--maxlenratio",
- type=float,
- default=0.0,
- help="Input length ratio to obtain max output length. "
- "If maxlenratio=0.0 (default), it uses a end-detect "
- "function "
- "to automatically find maximum hypothesis lengths."
- "If maxlenratio<0.0, its absolute value is interpreted"
- "as a constant max output length",
- )
- group.add_argument(
- "--minlenratio",
- type=float,
- default=0.0,
- help="Input length ratio to obtain min output length",
- )
- group.add_argument(
- "--ctc_weight",
- type=float,
- default=0.5,
- help="CTC weight in joint decoding",
- )
- group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
- group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
- group.add_argument("--streaming", type=str2bool, default=False)
-
- group.add_argument(
- "--frontend_conf",
- default=None,
- help="",
- )
- group.add_argument("--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",
- )
- group.add_argument(
- "--time_stamp_writer",
- type=str2bool,
- default=False,
- )
- 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()
--
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