def _init_vad_model(self): model_path = self._cfg.diarizer.vad.model_path if model_path.endswith('.nemo'): self._vad_model = EncDecClassificationModel.restore_from(model_path) logging.info("VAD model loaded locally from {}".format(model_path)) else: if model_path not in get_available_model_names(EncDecClassificationModel): logging.warning( "requested {} model name not available in pretrained models, instead".format(model_path) ) model_path = "vad_telephony_marblenet" logging.info("Loading pretrained {} model from NGC".format(model_path)) self._vad_model = EncDecClassificationModel.from_pretrained(model_name=model_path) self._vad_window_length_in_sec = self._cfg.diarizer.vad.window_length_in_sec self._vad_shift_length_in_sec = self._cfg.diarizer.vad.shift_length_in_sec self.has_vad_model_to_save = True self.has_vad_model = True
def _init_vad_model(self): if self._cfg.diarizer.vad.model_path.endswith('.nemo'): self._vad_model = EncDecClassificationModel.restore_from(self._cfg.diarizer.vad.model_path) self._vad_window_length_in_sec = self._cfg.diarizer.vad.window_length_in_sec self._vad_shift_length_in_sec = self._cfg.diarizer.vad.shift_length_in_sec self.has_vad_model_to_save = True self.has_vad_model = True else: raise ValueError("vad.model_path should be a .json file or .nemo or a .ckpt model file")