| | |
|---|
| | | proj_dim = self.encoder_conf["proj_dim"] |
|---|
| | | lorder = self.encoder_conf["lorder"] |
|---|
| | | for i in range(fsmn_layers): |
|---|
| | | cache = np.zeros(1, proj_dim, lorder-1, 1).astype(np.float32) |
|---|
| | | cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32) |
|---|
| | | in_cache.append(cache) |
|---|
| | | return in_cache |
|---|
| | | |
|---|
| | | |
|---|
| | | def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List: |
|---|
| | | waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq) |
|---|
| | | waveform_nums = len(waveform_list) |
|---|
| | | is_final = kwargs.get('kwargs', False) |
|---|
| | | |
|---|
| | | asr_res = [] |
|---|
| | | for beg_idx in range(0, waveform_nums, self.batch_size): |
|---|
| | | def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List: |
|---|
| | | # waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq) |
|---|
| | | |
|---|
| | | param_dict = kwargs.get('param_dict', dict()) |
|---|
| | | is_final = param_dict.get('is_final', False) |
|---|
| | | audio_in_cache = param_dict.get('audio_in_cache', None) |
|---|
| | | audio_in_cum = audio_in |
|---|
| | | if audio_in_cache is not None: |
|---|
| | | audio_in_cum = np.concatenate((audio_in_cache, audio_in_cum)) |
|---|
| | | param_dict['audio_in_cache'] = audio_in_cum |
|---|
| | | feats, feats_len = self.extract_feat([audio_in_cum]) |
|---|
| | | |
|---|
| | | in_cache = param_dict.get('in_cache', list()) |
|---|
| | | in_cache = self.prepare_cache(in_cache) |
|---|
| | | beg_idx = param_dict.get('beg_idx',0) |
|---|
| | | feats = feats[:, beg_idx:beg_idx+8, :] |
|---|
| | | param_dict['beg_idx'] = beg_idx + feats.shape[1] |
|---|
| | | try: |
|---|
| | | inputs = [feats] |
|---|
| | | inputs.extend(in_cache) |
|---|
| | | scores, out_caches = self.infer(inputs) |
|---|
| | | param_dict['in_cache'] = out_caches |
|---|
| | | segments = self.vad_scorer(scores, audio_in[None, :], is_final=is_final, max_end_sil=self.max_end_sil) |
|---|
| | | # print(segments) |
|---|
| | | if len(segments) == 1 and segments[0][0][1] != -1: |
|---|
| | | self.frontend.reset_status() |
|---|
| | | |
|---|
| | | end_idx = min(waveform_nums, beg_idx + self.batch_size) |
|---|
| | | waveform = waveform_list[beg_idx:end_idx] |
|---|
| | | feats, feats_len = self.extract_feat(waveform) |
|---|
| | | param_dict = kwargs.get('param_dict', dict()) |
|---|
| | | in_cache = param_dict.get('cache', list()) |
|---|
| | | in_cache = self.prepare_cache(in_cache) |
|---|
| | | try: |
|---|
| | | inputs = [feats] |
|---|
| | | inputs.extend(in_cache) |
|---|
| | | scores, out_caches = self.infer(inputs) |
|---|
| | | param_dict['cache'] = out_caches |
|---|
| | | segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil) |
|---|
| | | |
|---|
| | | except ONNXRuntimeError: |
|---|
| | | # logging.warning(traceback.format_exc()) |
|---|
| | | logging.warning("input wav is silence or noise") |
|---|
| | | segments = '' |
|---|
| | | asr_res.append(segments) |
|---|
| | | |
|---|
| | | except ONNXRuntimeError: |
|---|
| | | logging.warning(traceback.format_exc()) |
|---|
| | | logging.warning("input wav is silence or noise") |
|---|
| | | segments = [] |
|---|
| | | |
|---|
| | | return asr_res |
|---|
| | | return segments |
|---|
| | | |
|---|
| | | def load_data(self, |
|---|
| | | wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List: |
|---|
