export

游雁

2023-03-30 00bec8f243d5067f0b6719aacab52a73c2a530e8

export

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

@@ -226,7 +226,7 @@


@functools.lru_cache()
def get_logger(name='rapdi_paraformer'):
def get_logger(name='funasr_onnx'):
    """Initialize and get a logger by name.
    If the logger has not been initialized, this method will initialize the
    logger by adding one or two handlers, otherwise the initialized logger will

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

@@ -59,33 +59,38 @@
        
    
    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)
        # waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
        is_final = kwargs.get('kwargs', False)

        asr_res = []
        for beg_idx in range(0, waveform_nums, self.batch_size):
        param_dict = kwargs.get('param_dict', dict())
        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('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['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:
                # 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: