export

游雁

2023-03-29 c039cbc3bf3311c370d891c1bf67b275e95f0cd3

export

 funasr/runtime/python/onnxruntime/demo_vad.py

@@ -1,12 +1,30 @@

import soundfile
from funasr_onnx import Fsmn_vad


model_dir = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch"

wav_path = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav"
model = Fsmn_vad(model_dir)

wav_path = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav"
#offline vad
# result = model(wav_path)
# print(result)

result = model(wav_path)
print(result)
#online vad
speech, sample_rate = soundfile.read(wav_path)
speech_length = speech.shape[0]

sample_offset = 0
step = 160 * 10
param_dict = {'in_cache': []}
for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
    if sample_offset + step >= speech_length - 1:
        step = speech_length - sample_offset
        is_final = True
    else:
        is_final = False
    param_dict['is_final'] = is_final
    segments_result = model(audio_in=speech[sample_offset: sample_offset + step],
                            param_dict=param_dict)
    print(segments_result)


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

@@ -53,13 +53,13 @@
        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)
    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)

@@ -70,13 +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: