code: def _handle_long_waveform(
      w, filename, ID, ext, signal, rate, duration, max_length, index
  ):
      """
      Handle long audio waveforms by cutting them into pieces and writing to the CSV.
  
      Arguments
      ---------
          w: file
              The open CSV file for writing.
          filename: str
              The path to the audio file.
          ID: str
              The unique identifier for the audio.
          ext:  str
              The audio file extension.
          signal: Tensor
              The audio signal.
          rate: int
              The audio sample rate.
          duration:  float
              The duration of the audio in seconds.
          max_length:  float
              The maximum recording length in seconds.
          index: int
              The index of the audio file in the list.
      """
      os.remove(filename)
      for j in range(int(duration / max_length)):
          start = int(max_length * j * rate)
          stop = int(min(max_length * (j + 1), duration) * rate)
          ext = filename.split(".")[1]
          new_filename = filename.replace(
              "." + ext, "_" + str(j) + "." + ext
          )
  
          torchaudio.save(new_filename, signal[:, start:stop], rate)
          csv_row = (
              f"{ID}_{index}_{j}",
              str((stop - start) / rate),
              new_filename,
              ext,
              "\n",
          )
          w.write(",".join(csv_row))
  
file_path: speechbrain/speechbrain/augment/preparation.py
imports: import os
  import logging
  import torchaudio
  from speechbrain.utils.data_utils import download_file
  from speechbrain.utils.data_utils import get_all_files
label_desc: Use image node_id 6 for calling a global function with example usage: _handle_long_waveform(w, filename, ID, ext, signal, rate, duration, max_length, index) without return types