code: def main() -> None:
      args = get_args()
      if args.pretrained_name is None:
          model = (
              PunctuationCapitalizationModel.restore_from(
                  args.model_path
              )
              if not args.use_audio
              else PunctuationCapitalizationLexicalAudioModel.restore_from(
                  args.model_path
              )
          )
      else:
          model = (
              PunctuationCapitalizationModel.from_pretrained(
                  args.pretrained_name
              )
              if not args.use_audio
              else PunctuationCapitalizationLexicalAudioModel.restore_from(
                  args.model_path
              )
          )
      if args.device is None:
          if torch.cuda.is_available():
              model = model.cuda()
          else:
              model = model.cpu()
      else:
          model = model.to(args.device)
      if args.input_manifest is None:
          texts = []
          audios = []
          with args.input_text.open() as f:
              for line in f:
                  texts.append(line.strip())
          if args.use_audio:
              with args.audio_file.open() as f:
                  for line in f:
                      audios.append(line.strip())
      else:
          manifest = load_manifest(args.input_manifest)
          text_key = (
              "pred_text" if "pred_text" in manifest[0] else "text"
          )
          texts = []
          audios = []
          for item in manifest:
              texts.append(item[text_key])
              if args.use_audio:
                  audios.append(item["audio_filepath"])
      if args.use_audio:
          processed_texts = model.add_punctuation_capitalization(
              texts,
              batch_size=args.batch_size,
              max_seq_length=args.max_seq_length,
              step=args.step,
              margin=args.margin,
              return_labels=args.save_labels_instead_of_text,
              audio_queries=audios,
              target_sr=args.sample_rate,
          )
      else:
          processed_texts = model.add_punctuation_capitalization(
              texts,
              batch_size=args.batch_size,
              max_seq_length=args.max_seq_length,
              step=args.step,
              margin=args.margin,
              return_labels=args.save_labels_instead_of_text,
          )
      if args.output_manifest is None:
          args.output_text.parent.mkdir(exist_ok=True, parents=True)
          with args.output_text.open("w") as f:
              for t in processed_texts:
                  f.write(t + "\n")
      else:
          args.output_manifest.parent.mkdir(exist_ok=True, parents=True)
          with args.output_manifest.open("w") as f:
              for item, t in zip(manifest, processed_texts):
                  item[text_key] = t
                  f.write(json.dumps(item) + "\n")
  
file_path: NeMo/examples/nlp/token_classification/punctuate_capitalize_infer.py
imports: import argparse
  import json
  from pathlib import Path
  from typing import Dict, List, Union
  import torch.cuda
  from nemo.collections.nlp.models import PunctuationCapitalizationLexicalAudioModel, PunctuationCapitalizationModel
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types