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 load_model(weights_fpath, verbose=True):
      model = torch.load(weights_fpath)['model']
      model = model.remove_weightnorm(model)
      model.cuda().eval()
  
      return model
  
  
  def is_loaded(model):
      return model is not None
  
  
  def infer_waveform(mel,
                     model,
                     sigma=1.0,
                     is_fp16=False,
                     denoiser_strength=0.0):
      if is_fp16:
          from apex import amp
          model, _ = amp.initialize(model, [], opt_level="O3")
  
      # if denoiser_strength > 0:
      #     denoiser = Denoiser(_model).cuda()
  
      mel = torch.autograd.Variable(torch.tensor(mel).cuda())
      mel = torch.unsqueeze(mel, 0)
      mel = mel.half() if is_fp16 else mel
  
      # print(is_fp16)
      # print(type(mel))
  
      with torch.no_grad():
          audio = model.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')
  
      return audio
  
  
  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("-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()
  
      infer_waveform(args.filelist_path, args.waveglow_path, args.sigma,
           args.sampling_rate, args.is_fp16, args.denoiser_strength)
  