code: def _maybe_convert_wav(data_dir, original_data, converted_data):
      source_dir = os.path.join(data_dir, original_data)
      target_dir = os.path.join(data_dir, converted_data)
  
      # Conditionally convert sph files to wav files
      if os.path.exists(target_dir):
          print("skipping maybe_convert_wav")
          return
  
      # Create target_dir
      os.makedirs(target_dir)
  
      # Loop over sph files in source_dir and convert each to 16-bit PCM wav
      for root, dirnames, filenames in os.walk(source_dir):
          for filename in fnmatch.filter(filenames, "*.sph"):
              sph_file = os.path.join(root, filename)
              for channel in ["1", "2"]:
                  wav_filename = (
                      os.path.splitext(os.path.basename(sph_file))[0]
                      + "_c"
                      + channel
                      + ".wav"
                  )
                  wav_file = os.path.join(target_dir, wav_filename)
                  print(
                      "converting {} to {}".format(sph_file, wav_file)
                  )
                  subprocess.check_call(
                      [
                          "sph2pipe",
                          "-c",
                          channel,
                          "-p",
                          "-f",
                          "rif",
                          sph_file,
                          wav_file,
                      ]
                  )
  
file_path: DeepSpeech/bin/import_fisher.py
imports: import codecs
  import fnmatch
  import os
  import random
  import subprocess
  import sys
  import unicodedata
  import librosa
  import pandas
  import soundfile
  from deepspeech_training.util.importers import validate_label_eng
label_desc: Use image node_id 2 for calling a global function with example usage: _maybe_convert_wav(data_dir, original_data, converted_data) without return types