export

游雁

2023-03-29 a030ff0f85fd6b1cc2a1d443d2fcfb11ccb1aa8f

 funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py

@@ -23,7 +23,7 @@
                device_id: Union[str, int] = "-1",
                quantize: bool = False,
                intra_op_num_threads: int = 4,
                max_end_sil: int = 800,
                max_end_sil: int = None,
                ):
      
      if not Path(model_dir).exists():
@@ -43,20 +43,23 @@
      self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
      self.batch_size = batch_size
      self.vad_scorer = E2EVadModel(config["vad_post_conf"])
      self.max_end_sil = max_end_sil
      self.max_end_sil = max_end_sil if max_end_sil is not None else config["vad_post_conf"]["max_end_silence_time"]
      self.encoder_conf = config["encoder_conf"]
   
   def prepare_cache(self, in_cache: list = []):
      if len(in_cache) > 0:
         return in_cache
		
      for i in range(4):
         cache = np.random.rand(1, 128, 19, 1).astype(np.float32)
      fsmn_layers = self.encoder_conf["fsmn_layers"]
      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)
         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)
   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)
      waveform_nums = len(waveform_list)
      is_final = kwargs.get('kwargs', False)

@@ -67,13 +70,13 @@
         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 = param_dict.get('in_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
            param_dict['in_cache'] = out_caches
            segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
            
         except ONNXRuntimeError: