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kongdeqiang

2026-03-13 28ccfbfc51068a663a80764e14074df5edf2b5ba

 funasr/datasets/audio_datasets/preprocessor.py

@@ -17,35 +17,39 @@

@tables.register("preprocessor_classes", "SpeechPreprocessSpeedPerturb")
class SpeechPreprocessSpeedPerturb(nn.Module):
   def __init__(self, speed_perturb: list=None, **kwargs):
      super().__init__()
      self.speed_perturb = speed_perturb
		
   def forward(self, waveform, fs, **kwargs):
      if self.speed_perturb is None:
         return waveform
      speed = random.choice(self.speed_perturb)
      if speed != 1.0:
         if not isinstance(waveform, torch.Tensor):
            waveform = torch.tensor(waveform)
         waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
            waveform.view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]])
         waveform = waveform.view(-1)
			
      return waveform
    def __init__(self, speed_perturb: list = None, **kwargs):
        super().__init__()
        self.speed_perturb = speed_perturb

    def forward(self, waveform, fs, **kwargs):
        if self.speed_perturb is None:
            return waveform
        speed = random.choice(self.speed_perturb)
        if speed != 1.0:
            if not isinstance(waveform, torch.Tensor):
                waveform = torch.tensor(waveform)
            waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
                waveform.view(1, -1), fs, [["speed", str(speed)], ["rate", str(fs)]]
            )
            waveform = waveform.view(-1)

        return waveform


@tables.register("preprocessor_classes", "TextPreprocessSegDict")
class TextPreprocessSegDict(nn.Module):
   def __init__(self, seg_dict: str = None,
                text_cleaner: Collection[str] = None,
                split_with_space: bool = False,
                **kwargs):
      super().__init__()
		
      self.text_cleaner = TextCleaner(text_cleaner)
	
   def forward(self, text, **kwargs):
      text = self.text_cleaner(text)
		
      return text
    def __init__(
        self,
        seg_dict: str = None,
        text_cleaner: Collection[str] = None,
        split_with_space: bool = False,
        **kwargs
    ):
        super().__init__()

        self.text_cleaner = TextCleaner(text_cleaner)

    def forward(self, text, **kwargs):
        text = self.text_cleaner(text)

        return text