text: import os
  import numpy as np
  import json
  import random
  import torch
  from mel2samp import files_to_list, MAX_WAV_VALUE
  from denoiser import Denoiser
  from mel2samp import load_wav_to_torch
  from scipy.io.wavfile import write
  import resampy
  from tacotron2.layers import TacotronSTFT
  from glow import WaveGlow, WaveGlowLoss
  
  class Get_mel():
      def __init__(self, filter_length, hop_length, win_length,
                   sampling_rate, mel_fmin, mel_fmax):
          self.stft = TacotronSTFT(filter_length=filter_length,
                                   hop_length=hop_length,
                                   win_length=win_length,
                                   sampling_rate=sampling_rate,
                                   mel_fmin=mel_fmin, mel_fmax=mel_fmax)
      def get_mel(self, audio):
          audio_norm = audio / MAX_WAV_VALUE
          audio_norm = audio_norm.unsqueeze(0)
          audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
          melspec = self.stft.mel_spectrogram(audio_norm)
          melspec = torch.squeeze(melspec, 0)
          return melspec
  
  
  def main(style, wav_path, sigma, output_dir, sampling_rate, is_fp16,
           denoiser_strength, args):
      with open('config.json') as f:
          data = f.read()
      config = json.loads(data)
      waveglow_config = config["waveglow_config"]
      model = WaveGlow(**waveglow_config)
      checkpoint_dict = torch.load('waveglow_256channels_universal_v5.pt', map_location='cpu')
      model_for_loading = checkpoint_dict['model']
      model.load_state_dict(model_for_loading.state_dict())
      model.cuda()
      waveglow = model
      if is_fp16:
          from apex import amp
          waveglow, _ = amp.initialize(waveglow, [], opt_level="O1")
  
      if denoiser_strength > 0:
          denoiser = Denoiser(waveglow).cuda()
  
      mel_extractor = Get_mel(1024, 256, 1024, args.sampling_rate, 0.0, 8000.0)
      avg_style = np.load(style+'.npy')
      avg_style = torch.from_numpy(avg_style).float().cuda().unsqueeze(0).unsqueeze(-1)
      avg_style = avg_style.half() if is_fp16 else avg_style
  
      audio, rate = load_wav_to_torch(wav_path)
      if rate != sampling_rate:
          audio = resampy.resample(audio.numpy(), rate, sampling_rate)
          audio = torch.from_numpy(audio).float()
      mel = mel_extractor.get_mel(audio)
      audio = audio / MAX_WAV_VALUE
  
      mel = torch.autograd.Variable(mel.cuda().unsqueeze(0))
      audio = torch.autograd.Variable(audio.cuda().unsqueeze(0))
      audio = audio.half() if is_fp16 else audio
      mel = mel.half() if is_fp16 else mel
      outputs = waveglow((mel, audio))
      for ss in range(5):
          out_z = outputs[0] * (1 - ss*0.2) + avg_style * ss*0.2
          z = out_z[:,4:]
          mel_up = waveglow.upsample(mel)
          time_cutoff = waveglow.upsample.kernel_size[0]-waveglow.upsample.stride[0]
          mel_up = mel_up[:,:,:-time_cutoff]
  
          mel_up = mel_up.unfold(2, waveglow.n_group, waveglow.n_group).permute(0,2,1,3)
          mel_up = mel_up.contiguous().view(mel_up.size(0), mel_up.size(1), -1).permute(0, 2, 1)
          audio = z
          mel_up = mel_up[:,:,:audio.size(2)]
  
          sigma = 0.7
          z_i = 0
          for k in reversed(range(waveglow.n_flows)):
              n_half = int(audio.size(1)/2)
              audio_0 = audio[:,:n_half, :]
              audio_1 = audio[:, n_half:, :]
  
              output = waveglow.WN[k]((audio_0, mel_up))
  
              s = output[:,n_half:, :]
              b = output[:, :n_half, :]
              audio_1 = (audio_1-b)/torch.exp(s)
              audio = torch.cat([audio_0, audio_1],1)
  
              audio = waveglow.convinv[k](audio, reverse=True)
  
              if k % waveglow.n_early_every == 0 and k > 0:
                  z = out_z[:, 2-z_i:4-z_i]
                  audio = torch.cat((sigma*z, audio),1)
          audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
          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('transfer', ss))
          write(audio_path, sampling_rate, audio)
  
  
  
  if __name__ == "__main__":
      import argparse
  
      parser = argparse.ArgumentParser()
      parser.add_argument('-S', "--style", required=True)
      parser.add_argument('-w', '--wavepath',
                          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("--segment_length", default=16000, 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.style, args.wavepath, args.sigma, args.output_dir,
           args.sampling_rate, args.is_fp16, args.denoiser_strength, args)
  