code: def __text_normalization(json_file, num_workers=-1):
      text_normalizer_call_kwargs = {
          "punct_pre_process": True,
          "punct_post_process": True,
      }
      text_normalizer = Normalizer(
          lang="de",
          input_case="cased",
          overwrite_cache=True,
          cache_dir=str(json_file.parent / "cache_dir"),
      )
  
      def normalizer_call(x):
          return text_normalizer.normalize(
              x, **text_normalizer_call_kwargs
          )
  
      def add_normalized_text(line_dict):
          normalized_text = normalizer_call(line_dict["text"])
          line_dict.update({"normalized_text": normalized_text})
          return line_dict
  
      logging.info(f"Normalizing text for {json_file}.")
      with open(json_file, "r", encoding="utf-8") as fjson:
          lines = fjson.readlines()
          # Note: you need to verify which backend works well on your cluster.
          # backend="loky" is fine on multi-core Ubuntu OS; backend="threading" on Slurm.
          dict_list = Parallel(n_jobs=num_workers)(
              delayed(add_normalized_text)(json.loads(line))
              for line in tqdm(lines)
          )
  
      json_file_text_normed = (
          json_file.parent
          / f"{json_file.stem}_text_normed{json_file.suffix}"
      )
      with open(
          json_file_text_normed, "w", encoding="utf-8"
      ) as fjson_norm:
          for dct in dict_list:
              fjson_norm.write(json.dumps(dct) + "\n")
      logging.info(
          f"Normalizing text is complete: {json_file} --> {json_file_text_normed}"
      )
  
file_path: NeMo/scripts/dataset_processing/tts/hui_acg/get_data.py
imports: import argparse
  import json
  import random
  import shutil
  import urllib.request
  from pathlib import Path
  import pandas
  from joblib import Parallel, delayed
  from tqdm import tqdm
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling a global function with example usage: __text_normalization(json_file, num_workers) and returns: text_normalizer, line_dict