Shi Xian
2024-03-13 e04489ce4c0fd0095d0c79ef8f504f425e0435a8
funasr/models/fsmn_vad_streaming/model.py
@@ -644,49 +644,11 @@
      return results, meta_data
   
   def export(self, **kwargs):
      is_onnx = kwargs.get("type", "onnx") == "onnx"
      encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
      self.encoder = encoder_class(self.encoder, onnx=is_onnx)
      self.forward = self.export_forward
      return self
   def export_forward(self, feats: torch.Tensor, *args, **kwargs):
      scores, out_caches = self.encoder(feats, *args)
      return scores, out_caches
   def export_dummy_inputs(self, data_in=None, frame=30):
      if data_in is None:
         speech = torch.randn(1, frame, self.encoder_conf.get("input_dim"))
      else:
         speech = None # Undo
      cache_frames = self.encoder_conf.get("lorder") + self.encoder_conf.get("rorder") - 1
      in_cache0 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
      in_cache1 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
      in_cache2 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
      in_cache3 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
      return (speech, in_cache0, in_cache1, in_cache2, in_cache3)
   def export_input_names(self):
      return ['speech', 'in_cache0', 'in_cache1', 'in_cache2', 'in_cache3']
   def export_output_names(self):
      return ['logits', 'out_cache0', 'out_cache1', 'out_cache2', 'out_cache3']
   def export_dynamic_axes(self):
      return {
         'speech': {
            1: 'feats_length'
         },
      }
   def export_name(self, ):
      return "model.onnx"
      from .export_meta import export_rebuild_model
      models = export_rebuild_model(model=self, **kwargs)
      return models
   def DetectCommonFrames(self, cache: dict = {}) -> int:
      if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
         return 0