mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
support asr_inference_paraformer_vad_punc
This commit is contained in:
凌匀
parent
ebbde50a98
commit
8689fb676d
@ -144,7 +144,7 @@ class Speech2Text:
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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"Decoding device={device}, dtype={dtype}")
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# 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
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@ -184,12 +184,11 @@ class Speech2Text:
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self.encoder_downsampling_factor = 1
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if asr_train_args.encoder_conf["input_layer"] == "conv2d":
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self.encoder_downsampling_factor = 4
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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, begin_time: int = 0, end_time: int = None,
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self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
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begin_time: int = 0, end_time: int = None,
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):
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"""Inference
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@ -215,7 +214,7 @@ class Speech2Text:
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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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lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
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batch = {"speech": feats, "speech_lengths": feats_len}
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# a. To device
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@ -229,7 +228,8 @@ class Speech2Text:
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enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor
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predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], predictor_outs[2], predictor_outs[3]
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
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predictor_outs[2], predictor_outs[3]
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pre_token_length = pre_token_length.round().long()
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if torch.max(pre_token_length) < 1:
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return []
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@ -249,7 +249,7 @@ class Speech2Text:
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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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@ -260,23 +260,23 @@ class Speech2Text:
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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 and x != 2, 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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@ -286,12 +286,14 @@ class Speech2Text:
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timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
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results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
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else:
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time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, end_time)
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time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time,
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end_time)
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results.append((text, token, token_int, time_stamp, 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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class Speech2VadSegment:
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"""Speech2VadSegment class
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@ -333,6 +335,7 @@ class Speech2VadSegment:
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self.device = device
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self.dtype = dtype
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self.frontend = frontend
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self.batch_size = batch_size
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@torch.no_grad()
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def __call__(
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@ -361,56 +364,69 @@ class Speech2VadSegment:
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feats_len = feats_len.int()
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else:
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raise Exception("Need to extract feats first, please configure frontend configuration")
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batch = {"feats": feats, "feats_lengths": feats_len, "waveform": speech}
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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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segments = self.vad_model(**batch)
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# b. Forward Encoder streaming
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t_offset = 0
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step = min(feats_len, 6000)
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segments = [[]] * self.batch_size
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for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
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if t_offset + step >= feats_len - 1:
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step = feats_len - t_offset
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is_final_send = True
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else:
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is_final_send = False
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batch = {
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"feats": feats[:, t_offset:t_offset + step, :],
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"waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
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"is_final_send": is_final_send
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}
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# a. To device
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batch = to_device(batch, device=self.device)
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segments_part = self.vad_model(**batch)
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if segments_part:
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for batch_num in range(0, self.batch_size):
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segments[batch_num] += segments_part[batch_num]
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return fbanks, segments
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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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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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vad_infer_config: Optional[str] = None,
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vad_model_file: Optional[str] = None,
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vad_cmvn_file: Optional[str] = None,
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time_stamp_writer: bool = False,
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punc_infer_config: Optional[str] = None,
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punc_model_file: Optional[str] = None,
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**kwargs,
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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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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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vad_infer_config: Optional[str] = None,
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vad_model_file: Optional[str] = None,
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vad_cmvn_file: Optional[str] = None,
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time_stamp_writer: bool = False,
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punc_infer_config: Optional[str] = None,
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punc_model_file: Optional[str] = None,
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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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@ -449,63 +465,64 @@ def inference(
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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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cmvn_file: Optional[str] = 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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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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vad_infer_config: Optional[str] = None,
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vad_model_file: Optional[str] = None,
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vad_cmvn_file: Optional[str] = None,
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time_stamp_writer: bool = True,
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punc_infer_config: Optional[str] = None,
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punc_model_file: Optional[str] = None,
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outputs_dict: Optional[bool] = True,
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param_dict: dict = None,
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**kwargs,
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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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cmvn_file: Optional[str] = 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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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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vad_infer_config: Optional[str] = None,
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vad_model_file: Optional[str] = None,
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vad_cmvn_file: Optional[str] = None,
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time_stamp_writer: bool = True,
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punc_infer_config: Optional[str] = None,
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punc_model_file: Optional[str] = None,
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outputs_dict: Optional[bool] = True,
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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 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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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build speech2vadsegment
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speech2vadsegment_kwargs = dict(
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vad_infer_config=vad_infer_config,
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@ -516,7 +533,7 @@ def inference_modelscope(
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)
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# logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
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speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
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# 3. Build speech2text
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speech2text_kwargs = dict(
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asr_train_config=asr_train_config,
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@ -539,14 +556,14 @@ def inference_modelscope(
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)
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speech2text = Speech2Text(**speech2text_kwargs)
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text2punc = None
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if punc_model_file is not None:
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if punc_model_file is not None:
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text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
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if output_dir is not None:
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writer = DatadirWriter(output_dir)
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ibest_writer = writer[f"1best_recog"]
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ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
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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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@ -575,7 +592,7 @@ def inference_modelscope(
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use_timestamp = param_dict.get('use_timestamp', True)
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else:
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use_timestamp = True
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finish_count = 0
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file_count = 1
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lfr_factor = 6
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@ -586,13 +603,13 @@ def inference_modelscope(
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if output_path is not None:
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writer = DatadirWriter(output_path)
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ibest_writer = writer[f"1best_recog"]
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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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vad_results = speech2vadsegment(**batch)
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fbanks, vadsegments = vad_results[0], vad_results[1]
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for i, segments in enumerate(vadsegments):
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@ -606,19 +623,20 @@ def inference_modelscope(
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results = speech2text(**batch)
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if len(results) < 1:
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continue
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result_cur = [results[0][:-2]]
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if j == 0:
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result_segments = result_cur
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else:
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result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
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result_segments = [
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[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
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key = keys[0]
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result = result_segments[0]
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text, token, token_int = result[0], result[1], result[2]
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time_stamp = None if len(result) < 4 else result[3]
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if use_timestamp and time_stamp is not None:
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if use_timestamp and time_stamp is not None:
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postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
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else:
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postprocessed_result = postprocess_utils.sentence_postprocess(token)
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@ -635,13 +653,13 @@ def inference_modelscope(
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text_postprocessed_punc = text_postprocessed
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if len(word_lists) > 0 and text2punc is not None:
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text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
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item = {'key': key, 'value': text_postprocessed_punc}
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if text_postprocessed != "":
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item['text_postprocessed'] = text_postprocessed
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if time_stamp_postprocessed != "":
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item['time_stamp'] = time_stamp_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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@ -654,11 +672,13 @@ def inference_modelscope(
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ibest_writer["text_with_punc"][key] = text_postprocessed_punc
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if time_stamp_postprocessed is not None:
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ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
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logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
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return asr_result_list
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return _forward
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="ASR Decoding",
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@ -107,10 +107,8 @@ class Speech2VadSegment:
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feats_len = feats_len.int()
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else:
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raise Exception("Need to extract feats first, please configure frontend configuration")
|
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# batch = {"feats": feats, "waveform": speech, "is_final_send": True}
|
||||
# segments = self.vad_model(**batch)
|
||||
|
||||
# b. Forward Encoder sreaming
|
||||
# b. Forward Encoder streaming
|
||||
t_offset = 0
|
||||
step = min(feats_len, 6000)
|
||||
segments = [[]] * self.batch_size
|
||||
|
||||
364
vad_inference.py
364
vad_inference.py
@ -1,364 +0,0 @@
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
from typing import List
|
||||
from typing import Optional
|
||||
from typing import Sequence
|
||||
from typing import Tuple
|
||||
from typing import Union
|
||||
from typing import Dict
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from typeguard import check_argument_types
|
||||
from typeguard import check_return_type
|
||||
|
||||
from funasr.fileio.datadir_writer import DatadirWriter
|
||||
from funasr.modules.scorers.scorer_interface import BatchScorerInterface
|
||||
from funasr.modules.subsampling import TooShortUttError
|
||||
from funasr.tasks.vad import VADTask
|
||||
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
|
||||
|
||||
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 Speech2VadSegment:
|
||||
"""Speech2VadSegment class
|
||||
|
||||
Examples:
|
||||
>>> import soundfile
|
||||
>>> speech2segment = Speech2VadSegment("vad_config.yml", "vad.pt")
|
||||
>>> audio, rate = soundfile.read("speech.wav")
|
||||
>>> speech2segment(audio)
|
||||
[[10, 230], [245, 450], ...]
|
||||
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vad_infer_config: Union[Path, str] = None,
|
||||
vad_model_file: Union[Path, str] = None,
|
||||
vad_cmvn_file: Union[Path, str] = None,
|
||||
device: str = "cpu",
|
||||
batch_size: int = 1,
|
||||
dtype: str = "float32",
|
||||
**kwargs,
|
||||
):
|
||||
assert check_argument_types()
|
||||
|
||||
# 1. Build vad model
|
||||
vad_model, vad_infer_args = VADTask.build_model_from_file(
|
||||
vad_infer_config, vad_model_file, device
|
||||
)
|
||||
frontend = None
|
||||
if vad_infer_args.frontend is not None:
|
||||
frontend = WavFrontend(cmvn_file=vad_cmvn_file, **vad_infer_args.frontend_conf)
|
||||
|
||||
logging.info("vad_model: {}".format(vad_model))
|
||||
logging.info("vad_infer_args: {}".format(vad_infer_args))
|
||||
vad_model.to(dtype=getattr(torch, dtype)).eval()
|
||||
|
||||
self.vad_model = vad_model
|
||||
self.vad_infer_args = vad_infer_args
|
||||
self.device = device
|
||||
self.dtype = dtype
|
||||
self.frontend = frontend
|
||||
self.batch_size = batch_size
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(
|
||||
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
|
||||
) -> List[List[int]]:
|
||||
"""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()
|
||||
else:
|
||||
raise Exception("Need to extract feats first, please configure frontend configuration")
|
||||
|
||||
# b. Forward Encoder streaming
|
||||
t_offset = 0
|
||||
step = min(feats_len, 6000)
|
||||
segments = [[]] * self.batch_size
|
||||
for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
|
||||
if t_offset + step >= feats_len - 1:
|
||||
step = feats_len - t_offset
|
||||
is_final_send = True
|
||||
else:
|
||||
is_final_send = False
|
||||
batch = {
|
||||
"feats": feats[:, t_offset:t_offset + step, :],
|
||||
"waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
|
||||
"is_final_send": is_final_send
|
||||
}
|
||||
# a. To device
|
||||
batch = to_device(batch, device=self.device)
|
||||
segments_part = self.vad_model(**batch)
|
||||
if segments_part:
|
||||
for batch_num in range(0, self.batch_size):
|
||||
segments[batch_num] += segments_part[batch_num]
|
||||
return segments
|
||||
|
||||
|
||||
def inference(
|
||||
batch_size: int,
|
||||
ngpu: int,
|
||||
log_level: Union[int, str],
|
||||
data_path_and_name_and_type,
|
||||
vad_infer_config: Optional[str],
|
||||
vad_model_file: Optional[str],
|
||||
vad_cmvn_file: Optional[str] = None,
|
||||
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
||||
key_file: Optional[str] = None,
|
||||
allow_variable_data_keys: bool = False,
|
||||
output_dir: Optional[str] = None,
|
||||
dtype: str = "float32",
|
||||
seed: int = 0,
|
||||
num_workers: int = 1,
|
||||
**kwargs,
|
||||
):
|
||||
inference_pipeline = inference_modelscope(
|
||||
batch_size=batch_size,
|
||||
ngpu=ngpu,
|
||||
log_level=log_level,
|
||||
vad_infer_config=vad_infer_config,
|
||||
vad_model_file=vad_model_file,
|
||||
vad_cmvn_file=vad_cmvn_file,
|
||||
key_file=key_file,
|
||||
allow_variable_data_keys=allow_variable_data_keys,
|
||||
output_dir=output_dir,
|
||||
dtype=dtype,
|
||||
seed=seed,
|
||||
num_workers=num_workers,
|
||||
**kwargs,
|
||||
)
|
||||
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
|
||||
|
||||
|
||||
def inference_modelscope(
|
||||
batch_size: int,
|
||||
ngpu: int,
|
||||
log_level: Union[int, str],
|
||||
# data_path_and_name_and_type,
|
||||
vad_infer_config: Optional[str],
|
||||
vad_model_file: Optional[str],
|
||||
vad_cmvn_file: Optional[str] = None,
|
||||
# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
||||
key_file: Optional[str] = None,
|
||||
allow_variable_data_keys: bool = False,
|
||||
output_dir: Optional[str] = None,
|
||||
dtype: str = "float32",
|
||||
seed: int = 0,
|
||||
num_workers: int = 1,
|
||||
**kwargs,
|
||||
):
|
||||
assert check_argument_types()
|
||||
if batch_size > 1:
|
||||
raise NotImplementedError("batch decoding 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 speech2vadsegment
|
||||
speech2vadsegment_kwargs = dict(
|
||||
vad_infer_config=vad_infer_config,
|
||||
vad_model_file=vad_model_file,
|
||||
vad_cmvn_file=vad_cmvn_file,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
|
||||
speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
|
||||
|
||||
def _forward(
|
||||
data_path_and_name_and_type,
|
||||
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
||||
output_dir_v2: Optional[str] = None,
|
||||
fs: dict = None,
|
||||
param_dict: dict = None,
|
||||
):
|
||||
# 3. Build data-iterator
|
||||
loader = VADTask.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=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
|
||||
collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
|
||||
allow_variable_data_keys=allow_variable_data_keys,
|
||||
inference=True,
|
||||
)
|
||||
|
||||
finish_count = 0
|
||||
file_count = 1
|
||||
# 7 .Start for-loop
|
||||
# FIXME(kamo): The output format should be discussed about
|
||||
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
|
||||
if output_path is not None:
|
||||
writer = DatadirWriter(output_path)
|
||||
ibest_writer = writer[f"1best_recog"]
|
||||
else:
|
||||
writer = None
|
||||
ibest_writer = None
|
||||
|
||||
vad_results = []
|
||||
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}"
|
||||
|
||||
# do vad segment
|
||||
results = speech2vadsegment(**batch)
|
||||
for i, _ in enumerate(keys):
|
||||
results[i] = json.dumps(results[i])
|
||||
item = {'key': keys[i], 'value': results[i]}
|
||||
vad_results.append(item)
|
||||
if writer is not None:
|
||||
results[i] = json.loads(results[i])
|
||||
ibest_writer["text"][keys[i]] = "{}".format(results[i])
|
||||
|
||||
return vad_results
|
||||
|
||||
return _forward
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = config_argparse.ArgumentParser(
|
||||
description="VAD 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=False)
|
||||
parser.add_argument(
|
||||
"--ngpu",
|
||||
type=int,
|
||||
default=0,
|
||||
help="The number of gpus. 0 indicates CPU mode",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--gpuid_list",
|
||||
type=str,
|
||||
default="",
|
||||
help="The visible gpus",
|
||||
)
|
||||
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(
|
||||
"--vad_infer_config",
|
||||
type=str,
|
||||
help="VAD infer configuration",
|
||||
)
|
||||
group.add_argument(
|
||||
"--vad_model_file",
|
||||
type=str,
|
||||
help="VAD model parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--vad_cmvn_file",
|
||||
type=str,
|
||||
help="Global cmvn file",
|
||||
)
|
||||
|
||||
group = parser.add_argument_group("infer related")
|
||||
group.add_argument(
|
||||
"--batch_size",
|
||||
type=int,
|
||||
default=1,
|
||||
help="The batch size for inference",
|
||||
)
|
||||
|
||||
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()
|
||||
Loading…
Reference in New Issue
Block a user