class_docstr: *LibriTTS* :cite:`Zen2019LibriTTSAC` dataset.
  
  Args:
      root (str or Path): Path to the directory where the dataset is found or downloaded.
      url (str, optional): The URL to download the dataset from,
          or the type of the dataset to dowload.
          Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
          ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
          ``"train-other-500"``. (default: ``"train-clean-100"``)
      folder_in_archive (str, optional):
          The top-level directory of the dataset. (default: ``"LibriTTS"``)
      download (bool, optional):
          Whether to download the dataset if it is not found at root path. (default: ``False``).
code: def __init__(
      self,
      root: Union[str, Path],
      url: str = URL,
      folder_in_archive: str = FOLDER_IN_ARCHIVE,
      download: bool = False,
  ) -> None:
      if url in [
          "dev-clean",
          "dev-other",
          "test-clean",
          "test-other",
          "train-clean-100",
          "train-clean-360",
          "train-other-500",
      ]:
          ext_archive = ".tar.gz"
          base_url = "http://www.openslr.org/resources/60/"
  
          url = os.path.join(base_url, url + ext_archive)
  
      # Get string representation of 'root' in case Path object is passed
      root = os.fspath(root)
  
      basename = os.path.basename(url)
      archive = os.path.join(root, basename)
  
      basename = basename.split(".")[0]
      folder_in_archive = os.path.join(folder_in_archive, basename)
  
      self._path = os.path.join(root, folder_in_archive)
  
      if download:
          if not os.path.isdir(self._path):
              if not os.path.isfile(archive):
                  checksum = _CHECKSUMS.get(url, None)
                  download_url_to_file(
                      url, archive, hash_prefix=checksum
                  )
              _extract_tar(archive)
      else:
          if not os.path.exists(self._path):
              raise RuntimeError(
                  f"The path {self._path} doesn't exist. "
                  "Please check the ``root`` path or set `download=True` to download it"
              )
  
      self._walker = sorted(
          str(p.stem)
          for p in Path(self._path).glob("*/*/*" + self._ext_audio)
      )
  
file_path: audio/src/torchaudio/datasets/libritts.py
imports: import os
  from pathlib import Path
  from typing import 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.utils import _extract_tar
label_desc: Use image node_id 1 to create a new LIBRITTS object from inherited base classes: Dataset with example: obj = LIBRITTS(root, url, folder_in_archive, download)