code: def __process_data(data_folder, manifest_file, num_workers):
      files = []
      entries = []
  
      for root, dirnames, filenames in os.walk(data_folder):
          # we will use normalized text provided by the original dataset
          for filename in fnmatch.filter(filenames, "*.normalized.txt"):
              files.append(os.path.join(root, filename))
  
      with multiprocessing.Pool(num_workers) as p:
          processing_func = functools.partial(__process_transcript)
          results = p.imap(processing_func, files)
          for result in tqdm(results, total=len(files)):
              entries.extend(result)
  
      with open(manifest_file, "w") as fout:
          for m in entries:
              fout.write(json.dumps(m) + "\n")
  
file_path: NeMo/scripts/dataset_processing/tts/libritts/get_data.py
imports: import argparse
  import fnmatch
  import functools
  import json
  import multiprocessing
  import os
  import subprocess
  import tarfile
  import urllib.request
  from pathlib import Path
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: __process_data(data_folder, manifest_file, num_workers) without return types