| | |
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| | | # step.1: compute the vad model |
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| | | self.vad_kwargs.update(cfg) |
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| | | beg_vad = time.time() |
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| | | res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg) |
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| | | res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg) |
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| | | end_vad = time.time() |
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| | | print(f"time cost vad: {end_vad - beg_vad:0.3f}") |
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| | | |
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| | |
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| | | batch_size_ms_cum = 0 |
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| | | end_idx = j + 1 |
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| | | speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx]) |
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| | | results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg) |
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| | | results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg) |
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| | | if self.spk_model is not None: |
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| | | all_segments = [] |
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| | | # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]] |
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| | |
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| | | segments = sv_chunk(vad_segments) |
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| | | all_segments.extend(segments) |
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| | | speech_b = [i[2] for i in segments] |
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| | | spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg) |
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| | | spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg) |
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| | | results[_b]['spk_embedding'] = spk_res[0]['spk_embedding'] |
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| | | beg_idx = end_idx |
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| | | if len(results) < 1: |
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| | |
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| | | # step.3 compute punc model |
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| | | if self.punc_model is not None: |
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| | | self.punc_kwargs.update(cfg) |
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| | | punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg) |
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| | | punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg) |
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| | | result["text_with_punc"] = punc_res[0]["text"] |
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| | | |
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| | | # speaker embedding cluster after resorted |
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