code: def _maybe_convert_wav_dataset(extracted_dir, data_set):
      # Create source dir
      source_dir = path.join(extracted_dir, data_set, "sph")
  
      # Create target dir
      target_dir = path.join(extracted_dir, data_set, "wav")
  
      # Conditionally convert sph files to wav files
      if not gfile.Exists(target_dir):
          # Create target_dir
          makedirs(target_dir)
  
          # Loop over sph files in source_dir and convert each to wav
          for sph_file in glob(path.join(source_dir, "*.sph")):
              transformer = Transformer()
              wav_filename = (
                  path.splitext(path.basename(sph_file))[0] + ".wav"
              )
              wav_file = path.join(target_dir, wav_filename)
              transformer.build(sph_file, wav_file)
              remove(sph_file)
  
          # Remove source_dir
          rmdir(source_dir)
  
file_path: DeepSpeech/bin/import_ted.py
imports: import sys
  import tarfile
  import unicodedata
  import wave
  from glob import glob
  from os import makedirs, path, remove, rmdir
  import pandas
  from sox import Transformer
  from tensorflow.python.platform import gfile
  from deepspeech_training.util.downloader import maybe_download
  from deepspeech_training.util.stm import parse_stm_file
label_desc: Use image node_id 4 for calling a global function with example usage: _maybe_convert_wav_dataset(extracted_dir, data_set) without return types