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| | | def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List: |
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| | | # waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq) |
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| | | |
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| | | param_dict = kwargs.get('param_dict', dict()) |
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| | | is_final = param_dict.get('is_final', False) |
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| | | audio_in_cache = param_dict.get('audio_in_cache', None) |
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| | | audio_in_cum = audio_in |
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| | | if audio_in_cache is not None: |
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| | | audio_in_cum = np.concatenate((audio_in_cache, audio_in_cum)) |
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| | | param_dict['audio_in_cache'] = audio_in_cum |
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| | | feats, feats_len = self.extract_feat([audio_in_cum]) |
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| | | |
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| | | in_cache = param_dict.get('in_cache', list()) |
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| | | in_cache = self.prepare_cache(in_cache) |
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| | | beg_idx = param_dict.get('beg_idx',0) |
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| | | feats = feats[:, beg_idx:beg_idx+8, :] |
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| | | param_dict['beg_idx'] = beg_idx + feats.shape[1] |
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| | | try: |
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| | | inputs = [feats] |
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| | | inputs.extend(in_cache) |
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| | | scores, out_caches = self.infer(inputs) |
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| | | param_dict['in_cache'] = out_caches |
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| | | segments = self.vad_scorer(scores, audio_in[None, :], is_final=is_final, max_end_sil=self.max_end_sil) |
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| | | # print(segments) |
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| | | if len(segments) == 1 and segments[0][0][1] != -1: |
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| | | self.frontend.reset_status() |
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| | | waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq) |
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| | | waveform_nums = len(waveform_list) |
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| | | is_final = kwargs.get('kwargs', False) |
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| | | |
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| | | segments = [[]] * self.batch_size |
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| | | for beg_idx in range(0, waveform_nums, self.batch_size): |
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| | | |
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| | | |
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| | | except ONNXRuntimeError: |
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| | | logging.warning(traceback.format_exc()) |
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| | | logging.warning("input wav is silence or noise") |
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| | | segments = [] |
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| | | end_idx = min(waveform_nums, beg_idx + self.batch_size) |
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| | | waveform = waveform_list[beg_idx:end_idx] |
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| | | feats, feats_len = self.extract_feat(waveform) |
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| | | waveform = np.array(waveform) |
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| | | param_dict = kwargs.get('param_dict', dict()) |
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| | | in_cache = param_dict.get('in_cache', list()) |
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| | | in_cache = self.prepare_cache(in_cache) |
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| | | try: |
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| | | t_offset = 0 |
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| | | step = int(min(feats_len.max(), 6000)) |
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| | | for t_offset in range(0, int(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 = True |
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| | | else: |
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| | | is_final = False |
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| | | feats_package = feats[:, t_offset:int(t_offset + step), :] |
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| | | waveform_package = waveform[:, t_offset * 160:min(waveform.shape[-1], (int(t_offset + step) - 1) * 160 + 400)] |
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| | | |
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| | | inputs = [feats_package] |
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| | | # inputs = [feats] |
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| | | inputs.extend(in_cache) |
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| | | scores, out_caches = self.infer(inputs) |
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| | | in_cache = out_caches |
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| | | segments_part = self.vad_scorer(scores, waveform_package, is_final=is_final, max_end_sil=self.max_end_sil, online=False) |
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| | | # segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil) |
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| | | |
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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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| | | except ONNXRuntimeError: |
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| | | # logging.warning(traceback.format_exc()) |
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| | | logging.warning("input wav is silence or noise") |
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| | | segments = '' |
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| | | |
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| | | return segments |
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| | | |
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| | |
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| | | outputs = self.ort_infer(feats) |
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| | | scores, out_caches = outputs[0], outputs[1:] |
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| | | return scores, out_caches |
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