def METHOD_NAME(unused_argv=None):
  os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu_number)
  source_path = utils.shell_path(FLAGS.source_path)
  checkpoint_path = utils.shell_path(FLAGS.checkpoint_path)
  save_path = utils.shell_path(FLAGS.save_path)
  if not save_path:
    raise ValueError("Must specify a save_path.")
  tf.logging.set_verbosity(FLAGS.log)
  # Use directory of files
  if tf.gfile.IsDirectory(source_path):
    files = tf.gfile.ListDirectory(source_path)
    file_extensions = [os.path.splitext(f)[1] for f in files]
    if ".wav" in file_extensions:
      file_extension = ".wav"
    elif ".npy" in file_extensions:
      file_extension = ".npy"
    else:
      raise RuntimeError("Folder must contain .wav or .npy files.")
    file_extension = ".npy" if FLAGS.npy_only else file_extension
    files = sorted([
        os.path.join(source_path, fname)
        for fname in files
        if fname.lower().endswith(file_extension)
    ])
  # Use a single file
  elif source_path.lower().endswith((".wav", ".npy")):
    file_extension = os.path.splitext(source_path.lower())[1]
    files = [source_path]
  else:
    raise ValueError(
        "source_path {} must be a folder or file.".format(source_path))
  # Now synthesize from files one batch at a time
  batch_size = FLAGS.batch_size
  sample_length = FLAGS.sample_length
  n = len(files)
  for start in range(0, n, batch_size):
    end = start + batch_size
    batch_files = files[start:end]
    save_names = [
        os.path.join(save_path,
                     "gen_" + os.path.splitext(os.path.basename(f))[0] + ".wav")
        for f in batch_files
    ]
    # Encode waveforms
    if file_extension == ".wav":
      batch_data = fastgen.load_batch_audio(
          batch_files, sample_length=sample_length)
      encodings = fastgen.encode(
          batch_data, checkpoint_path, sample_length=sample_length)
    # Or load encodings
    else:
      encodings = fastgen.load_batch_encodings(
          batch_files, sample_length=sample_length)
    # Synthesize multi-gpu
    if FLAGS.gpu_number != 0:
      with tf.device("/device:GPU:%d" % FLAGS.gpu_number):
        fastgen.synthesize(
            encodings, save_names, checkpoint_path=checkpoint_path)
    # Single gpu
    else:
      fastgen.synthesize(
          encodings, save_names, checkpoint_path=checkpoint_path)