code: def __process_data(data_root):
      text_normalizer = Normalizer(
          lang="en",
          input_case="cased",
          overwrite_cache=True,
          cache_dir=data_root / "cache_dir",
      )
      text_normalizer_call_kwargs = {
          "punct_pre_process": True,
          "punct_post_process": True,
      }
      normalizer_call = lambda x: text_normalizer.normalize(
          x, **text_normalizer_call_kwargs
      )
  
      # Create manifests (based on predefined NVIDIA's split)
      filelists = ["train", "val", "test"]
      for split in tqdm(filelists):
          # Download file list if necessary
          filelist_path = (
              data_root / f"ljs_audio_text_{split}_filelist.txt"
          )
  
          if not filelist_path.exists():
              wget.download(
                  f"{FILELIST_BASE}/ljs_audio_text_{split}_filelist.txt",
                  out=str(data_root),
              )
  
          manifest_target = data_root / f"{split}_manifest.json"
          with open(manifest_target, "w") as f_out:
              with open(filelist_path, "r") as filelist:
                  print(f"\nCreating {manifest_target}...")
                  for line in tqdm(filelist):
                      basename = line[6:16]
  
                      text = line[21:].strip()
                      norm_text = normalizer_call(text)
  
                      # Make sure corresponding wavfile exists
                      wav_path = data_root / "wavs" / f"{basename}.wav"
                      assert (
                          wav_path.exists()
                      ), f"{wav_path} does not exist!"
  
                      entry = {
                          "audio_filepath": str(wav_path),
                          "duration": sox.file_info.duration(wav_path),
                          "text": text,
                          "normalized_text": norm_text,
                      }
  
                      f_out.write(json.dumps(entry) + "\n")
  
file_path: NeMo/scripts/dataset_processing/tts/ljspeech/get_data.py
imports: import argparse
  import json
  import tarfile
  import urllib.request
  from pathlib import Path
  import sox
  import wget
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: __process_data(data_root) without return types