code: def _get_librispeech_metadata(
      fileid: str, root: str, folder: str, ext_audio: str, ext_txt: str
  ) -> Tuple[str, int, str, int, int, int]:
      speaker_id, chapter_id, utterance_id = fileid.split("-")
  
      # Get audio path and sample rate
      fileid_audio = f"{speaker_id}-{chapter_id}-{utterance_id}"
      filepath = os.path.join(
          folder, speaker_id, chapter_id, f"{fileid_audio}{ext_audio}"
      )
  
      # Load text
      file_text = f"{speaker_id}-{chapter_id}{ext_txt}"
      file_text = os.path.join(
          root, folder, speaker_id, chapter_id, file_text
      )
      with open(file_text) as ft:
          for line in ft:
              fileid_text, transcript = line.strip().split(" ", 1)
              if fileid_audio == fileid_text:
                  break
          else:
              # Translation not found
              raise FileNotFoundError(
                  f"Translation not found for {fileid_audio}"
              )
  
      return (
          filepath,
          SAMPLE_RATE,
          transcript,
          int(speaker_id),
          int(chapter_id),
          int(utterance_id),
      )
  
file_path: audio/src/torchaudio/datasets/librispeech.py
imports: import os
  from pathlib import Path
  from typing import Tuple, Union
  from torch import Tensor
  from torch.utils.data import Dataset
  from torchaudio._internal import download_url_to_file
  from torchaudio.datasets.utils import _extract_tar, _load_waveform
label_desc: Use image node_id 2 for calling a global function with example usage: _get_librispeech_metadata(fileid, root, folder, ext_audio, ext_txt) and returns: filepath,  SAMPLE_RATE,  transcript,  int,  int,  int