code: def create_enhance_csv(data_folder, csv_file, split, fs=16000):
      """
      Create CSV files for train, valid and test set.
  
      Arguments:
      ----------
      data_folder :str
          Path to synthesized RescuSpeech data for task enhancement
      csv_file : str
          Save csv_file path for prepared data.
      split : str
          CSV prepration for train/valid/test
      fs : int
          Sampling rate. Defaults to 16000.
      """
  
      clean_fullpaths = []
      noise_fullpaths = []
      noisy_fullpaths = []
      language = []
      lang = "de"
  
      clean_f1_path = extract_files(
          os.path.join(data_folder, split), type="clean"
      )
      noise_f1_path = extract_files(
          os.path.join(data_folder, split), type="noise"
      )
      noisy_f1_path = extract_files(
          os.path.join(data_folder, split), type="noisy"
      )
  
      language.extend([lang] * len(clean_f1_path))
      clean_fullpaths.extend(clean_f1_path)
      noise_fullpaths.extend(noise_f1_path)
      noisy_fullpaths.extend(noisy_f1_path)
  
      # Write CSV for train and dev
      msg = "Writing " + split + " csv files"
      logger.info(msg)
      write2csv(
          language,
          clean_fullpaths,
          noise_fullpaths,
          noisy_fullpaths,
          csv_file,
          fs,
      )
  
file_path: speechbrain/recipes/RescueSpeech/ASR/noise-robust/rescuespeech_prepare.py
imports: import os
  import re
  import csv
  import glob
  import logging
  import torchaudio
  import unicodedata
  from tqdm import tqdm
  from tqdm.contrib import tzip
  from speechbrain.dataio.dataio import read_audio
label_desc: Use image node_id 4 for calling a global function with example usage: create_enhance_csv(data_folder, csv_file, split, fs) without return types