| | |
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| | | |
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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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| | | self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, |
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| | | in_cache: Dict[str, torch.Tensor] = dict() |
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| | | ) -> Tuple[List[List[int]], Dict[str, torch.Tensor]]: |
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| | | """Inference |
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| | | |
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| | |
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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": is_final |
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| | | "is_final": is_final, |
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| | | "in_cache": in_cache |
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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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| | | segments_part, in_cache = 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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| | |
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| | | # do vad segment |
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| | | _, results = speech2vadsegment(**batch) |
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| | | for i, _ in enumerate(keys): |
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| | | results[i] = json.dumps(results[i]) |
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| | | # results[i] = json.dumps(results[i]) |
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| | | item = {'key': keys[i], 'value': results[i]} |
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| | | vad_results.append(item) |
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| | | if writer is not None: |
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