text: # *****************************************************************************
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  # *****************************************************************************
  import os
  from scipy.io.wavfile import write
  import torch
  from mel2samp import files_to_list, MAX_WAV_VALUE
  from denoiser import Denoiser
  
  
  def main(mel_files, waveglow_path, sigma, output_dir, sampling_rate, is_fp16,
           denoiser_strength):
      mel_files = files_to_list(mel_files)
      waveglow = torch.load(waveglow_path, map_location='cpu')['model']
      waveglow = waveglow.remove_weightnorm(waveglow)
      waveglow.cpu().eval()
      if is_fp16:
          from apex import amp
          waveglow, _ = amp.initialize(waveglow, [], opt_level="O3")
  
      if denoiser_strength > 0:
          denoiser = Denoiser(waveglow).cpu()
  
      for i, file_path in enumerate(mel_files):
          file_name = os.path.splitext(os.path.basename(file_path))[0]
          mel = torch.load(file_path, map_location='cpu')
          mel = torch.autograd.Variable(mel.cpu())
          mel = torch.unsqueeze(mel, 0)
          mel = mel.half() if is_fp16 else mel
          with torch.no_grad():
              audio = waveglow.infer(mel, sigma=sigma)
              if denoiser_strength > 0:
                  audio = denoiser(audio, denoiser_strength)
              audio = audio * MAX_WAV_VALUE
          audio = audio.squeeze()
          audio = audio.cpu().numpy()
          audio = audio.astype('int16')
          audio_path = os.path.join(
              output_dir, "{}_synthesis.wav".format(file_name))
          write(audio_path, sampling_rate, audio)
          print(audio_path)
  
  
  if __name__ == "__main__":
      import argparse
  
      parser = argparse.ArgumentParser()
      parser.add_argument('-f', "--filelist_path", required=True)
      parser.add_argument('-w', '--waveglow_path',
                          help='Path to waveglow decoder checkpoint with model')
      parser.add_argument('-o', "--output_dir", required=True)
      parser.add_argument("-s", "--sigma", default=1.0, type=float)
      parser.add_argument("--sampling_rate", default=22050, type=int)
      parser.add_argument("--is_fp16", action="store_true")
      parser.add_argument("-d", "--denoiser_strength", default=0.0, type=float,
                          help='Removes model bias. Start with 0.1 and adjust')
  
      args = parser.parse_args()
  
      main(args.filelist_path, args.waveglow_path, args.sigma, args.output_dir,
           args.sampling_rate, args.is_fp16, args.denoiser_strength)
  