code: def create_wham_noise_dataframe(wham_noise_dir, subdir):
      """Generate a dataframe that gather infos about the sound files in a
      wham_noise subdirectory"""
  
      print(f"Processing files from {subdir} dir")
      # Get the current directory path
      dir_path = os.path.join(wham_noise_dir, subdir)
      # Recursively look for .wav files in current directory
      sound_paths = glob.glob(
          os.path.join(dir_path, "**/*.wav"), recursive=True
      )
      # Create the dataframe corresponding to this directory
      dir_md = pd.DataFrame(
          columns=[
              "noise_ID",
              "subset",
              "length",
              "augmented",
              "origin_path",
          ]
      )
  
      # Go through the sound file list
      for sound_path in tqdm(sound_paths, total=len(sound_paths)):
          # Get the ID of the noise file
          noise_id = os.path.split(sound_path)[1]
          # Get its length
          length = len(sf.SoundFile(sound_path))
          augment = False
          if "sp08" in sound_path or "sp12" in sound_path:
              augment = True
          # Get the sound file relative path
          rel_path = os.path.relpath(sound_path, wham_noise_dir)
          # Add information to the dataframe
          dir_md.loc[len(dir_md)] = [
              noise_id,
              subdir,
              length,
              augment,
              rel_path,
          ]
      return dir_md
  
file_path: speechbrain/recipes/Aishell1Mix/separation/scripts/create_wham_metadata.py
imports: import os
  import argparse
  import soundfile
  import pandas
  import glob
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: create_wham_noise_dataframe(wham_noise_dir, subdir) and returns: dir_md