class_docstr: Subset of Libri-light :cite:`librilight` dataset,
  which was used in HuBERT :cite:`hsu2021hubert` for supervised fine-tuning.
  
  Args:
      root (str or Path): Path to the directory where the dataset is found or downloaded.
      subset (str, optional): The subset to use. Options: [``"10min"``, ``"1h"``, ``"10h"``]
          (Default: ``"10min"``).
      download (bool, optional):
          Whether to download the dataset if it is not found at root path. (default: ``False``).
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docstr: Load the n-th sample from the dataset.
  
  Args:
      n (int): The index of the sample to be loaded
  Returns:
      Tuple of the following items;
  
      Tensor:
          Waveform
      int:
          Sample rate
      str:
          Transcript
      int:
          Speaker ID
      int:
          Chapter ID
      int:
          Utterance ID
docstr_tok: ["Load","the","n-th","sample","from","the","dataset",".","Args",":","n","(","int",")",":","The","index","of","the","sample","to","be","loaded","Returns",":","Tuple","of","the","following","items",";","Tensor",":","Waveform","int",":","Sample","rate","str",":","Transcript","int",":","Speaker","ID","int",":","Chapter","ID","int",":","Utterance","ID"]
code: def __getitem__(
      self, n: int
  ) -> Tuple[Tensor, int, str, int, int, int]:
      """Load the n-th sample from the dataset.
  
      Args:
          n (int): The index of the sample to be loaded
      Returns:
          Tuple of the following items;
  
          Tensor:
              Waveform
          int:
              Sample rate
          str:
              Transcript
          int:
              Speaker ID
          int:
              Chapter ID
          int:
              Utterance ID
      """
      file_path, fileid = self._fileids_paths[n]
      metadata = _get_librispeech_metadata(
          fileid, self._path, file_path, self._ext_audio, self._ext_txt
      )
      waveform, _ = torchaudio.load(
          os.path.join(self._path, metadata[0])
      )
      return (waveform,) + metadata[1:]
  
file_path: audio/src/torchaudio/datasets/librilight_limited.py
imports: import os
  from pathlib import Path
  from typing import List, Tuple, Union
  import torchaudio
  from torch import Tensor
  from torch.utils.data import Dataset
  from torchaudio._internal import download_url_to_file
  from torchaudio.datasets.librispeech import _get_librispeech_metadata
  from torchaudio.datasets.utils import _extract_tar
label_desc: Use image node_id 2 for calling the LibriLightLimited obj's underlying member method code with example usage: obj.__getitem__(n) and returns: unknown