text: import sys
  sys.path.append('tacotron2')
  import torch
  from mellotron.layers import STFT
  
  _device = 'cuda' if torch.cuda.is_available() else 'cpu'
  
  class Denoiser(torch.nn.Module):
      """ Removes model bias from audio produced with waveglow """
  
      def __init__(self, waveglow, filter_length=1024, n_overlap=4,
                   win_length=1024, mode='zeros'):
          super(Denoiser, self).__init__()
          self.stft = STFT(filter_length=filter_length,
                           hop_length=int(filter_length/n_overlap),
                           win_length=win_length).to(_device)
          if mode == 'zeros':
              mel_input = torch.zeros(
                  (1, 80, 88),
                  dtype=waveglow.upsample.weight.dtype,
                  device=waveglow.upsample.weight.device)
          elif mode == 'normal':
              mel_input = torch.randn(
                  (1, 80, 88),
                  dtype=waveglow.upsample.weight.dtype,
                  device=waveglow.upsample.weight.device)
          else:
              raise Exception("Mode {} if not supported".format(mode))
  
          with torch.no_grad():
              bias_audio = waveglow.infer(mel_input, sigma=0.0).float()
              bias_spec, _ = self.stft.transform(bias_audio)
  
          self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None])
  
      def forward(self, audio, strength=0.1):
          audio_spec, audio_angles = self.stft.transform(audio.to(_device).float())
          audio_spec_denoised = audio_spec - self.bias_spec * strength
          audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
          audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles)
          return audio_denoised
  