code: def process_google_speech_train(data_dir):
      X = []
      for o in os.listdir(data_dir):
          if (
              os.path.isdir(os.path.join(data_dir, o))
              and o.split("/")[-1] != "_background_noise_"
          ):
              X.extend(glob.glob(os.path.join(data_dir, o) + "/*.wav"))
  
      short_files = [i.split(data_dir)[1] for i in files]
  
      with open(
          os.path.join(data_dir, "testing_list.txt"), "r"
      ) as allfile:
          testing_list = allfile.read().splitlines()
  
      with open(
          os.path.join(data_dir, "validation_list.txt"), "r"
      ) as allfile:
          validation_list = allfile.read().splitlines()
  
      exist_set = set(testing_list).copy()
      exist_set.update(set(validation_list))
  
      training_list = [i for i in short_files if i not in exist_set]
  
      with open(
          os.path.join(data_dir, "training_list.txt"), "w"
      ) as outfile:
          outfile.write("\n".join(training_list))
  
      logging.info(
          f"Overall: {len(files)}, Train: {len(training_list)}, Validatoin: {len(validation_list)}, Test: {len(testing_list)}"
      )
  
imports: import argparse
  import glob
  import json
  import logging
  import os
  import tarfile
  import urllib.request
  import librosa
  import numpy
  import soundfile
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 5 for calling a global function with example usage: process_google_speech_train(data_dir) without return types