text: from synthesizer.preprocess import create_embeddings
  from utils.argutils import print_args
  from pathlib import Path
  import argparse
  
  from synthesizer.preprocess import preprocess_dataset
  from synthesizer.hparams import hparams
  from utils.argutils import print_args
  from pathlib import Path
  import argparse
  
  recognized_datasets = [
      "aidatatang_200zh",
      "magicdata",
      "aishell3",
      "data_aishell"
  ]
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(
          description="Preprocesses audio files from datasets, encodes them as mel spectrograms "
                      "and writes them to  the disk. Audio files are also saved, to be used by the "
                      "vocoder for training.",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter
      )
      parser.add_argument("datasets_root", type=Path, help=\
          "Path to the directory containing your datasets.")
      parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
          "Path to the output directory that will contain the mel spectrograms, the audios and the "
          "embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
      parser.add_argument("-n", "--n_processes", type=int, default=1, help=\
          "Number of processes in parallel.")
      parser.add_argument("-s", "--skip_existing", action="store_true", help=\
          "Whether to overwrite existing files with the same name. Useful if the preprocessing was "
          "interrupted. ")
      parser.add_argument("--hparams", type=str, default="", help=\
          "Hyperparameter overrides as a comma-separated list of name-value pairs")
      parser.add_argument("--no_trim", action="store_true", help=\
          "Preprocess audio without trimming silences (not recommended).")
      parser.add_argument("--no_alignments", action="store_true", help=\
          "Use this option when dataset does not include alignments\
          (these are used to split long audio files into sub-utterances.)")
      parser.add_argument("-d", "--dataset", type=str, default="aidatatang_200zh", help=\
          "Name of the dataset to process, allowing values: magicdata, aidatatang_200zh, aishell3.")
      parser.add_argument("-e", "--encoder_model_fpath", type=Path, default="encoder/saved_models/pretrained.pt", help=\
          "Path your trained encoder model.")
      parser.add_argument("-ne", "--n_processes_embed", type=int, default=1, help=\
          "Number of processes in parallel.An encoder is created for each, so you may need to lower "
          "this value on GPUs with low memory. Set it to 1 if CUDA is unhappy")
      args = parser.parse_args()
  
      # Process the arguments
      if not hasattr(args, "out_dir"):
          args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
      assert args.dataset in recognized_datasets, 'is not supported, please vote for it in https://github.com/babysor/MockingBird/issues/10'
      # Create directories
      assert args.datasets_root.exists()
      args.out_dir.mkdir(exist_ok=True, parents=True)
  
      # Verify webrtcvad is available
      if not args.no_trim:
          try:
              import webrtcvad
          except:
              raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
                  "noise removal and is recommended. Please install and try again. If installation fails, "
                  "use --no_trim to disable this error message.")
      encoder_model_fpath = args.encoder_model_fpath
      del args.no_trim, args.encoder_model_fpath
     
      args.hparams = hparams.parse(args.hparams)
      n_processes_embed = args.n_processes_embed
      del args.n_processes_embed
      preprocess_dataset(**vars(args))
      
      create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath)    
  