code: def generate_variety_noise(data_dir, filename, prefix):
      curr_dir = data_dir.split("_background_noise_")[0]
      silence_path = os.path.join(curr_dir, "_background_noise_more")
  
      if not os.path.exists(silence_path):
          os.mkdir(silence_path)
  
      silence_stride = 1000  # stride = 1/16 seconds
      sampling_rate = 16000
  
      silence_files = []
      rng = np.random.RandomState(0)
  
      filename = prefix + "_" + filename
      file_path = os.path.join(data_dir, filename)
  
      with open(file_path, "r") as allfile:
          files = allfile.read().splitlines()
  
      for file in files:
          y, sr = librosa.load(path=file, sr=sampling_rate)
  
          for i in range(
              0, len(y) - sampling_rate, silence_stride * 100
          ):  # stride * 100 to generate less samples for demo
              file_name = "{}_{}.wav".format(file.split("/")[-1], i)
              y_slice = y[i : i + sampling_rate]
              magnitude = rng.uniform(0.0, 1.0)
              y_slice *= magnitude
              out_file_path = os.path.join(silence_path, file_name)
              sf.write(out_file_path, y_slice, sr)
  
              silence_files.append(out_file_path)
  
      new_list_file = os.path.join(silence_path, filename)
      with open(new_list_file, "w") as outfile:
          outfile.write("\n".join(silence_files))
  
      logging.info(
          f"Generate {len(out_file_path)} background files for {file_path}. => {new_list_file} !"
      )
      return len(silence_files)
  
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 9 for calling a global function with example usage: generate_variety_noise(data_dir, filename, prefix) and returns: len