code: def create_wham_noise_metadata(wham_noise_dir, md_dir):
      """Generate metadata corresponding to downloaded data in wham_noise"""
  
      # Check already generated files
      not_already_processed_dir = check_already_generated(md_dir)
      # Go through each directory and create associated metadata
      for ldir in not_already_processed_dir:
          # Generate the dataframe relative to the directory
          dir_metadata = create_wham_noise_dataframe(
              wham_noise_dir, ldir
          )
          # Sort the dataframe according to ascending Length
          dir_metadata = dir_metadata.sort_values("length")
          # Write the dataframe in a .csv in the metadata directory
          if ldir == "tt":
              name = "test"
          elif ldir == "cv":
              name = "dev"
          else:
              name = "train"
          # Filter out files that are shorter than 3s
          num_samples = NUMBER_OF_SECONDS * RATE
          dir_metadata = dir_metadata[
              dir_metadata["length"] >= num_samples
          ]
          # Create save path
          save_path = os.path.join(md_dir, name + ".csv")
          print(f"Medatada file created in {save_path}")
          dir_metadata.to_csv(save_path, index=False)
  
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 2 for calling a global function with example usage: create_wham_noise_metadata(wham_noise_dir, md_dir) without return types