class_docstr: *LibriSpeech* :cite:`7178964` dataset with prefix-tree construction and biasing support.
  
  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: ``"LibriSpeech"``)
      download (bool, optional):
          Whether to download the dataset if it is not found at root path. (default: ``False``).
      blist (list, optional):
          The list of biasing words (default: ``[]``).
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code: def __init__(
      self,
      root: Union[str, Path],
      url: str = URL,
      folder_in_archive: str = FOLDER_IN_ARCHIVE,
      download: bool = False,
      blist: List[str] = None,
  ) -> None:
      self._url = url
      if url not in _DATA_SUBSETS:
          raise ValueError(
              f"Invalid url '{url}' given; please provide one of {_DATA_SUBSETS}."
          )
  
      root = os.fspath(root)
      self._archive = os.path.join(root, folder_in_archive)
      self._path = os.path.join(root, folder_in_archive, url)
  
      if not os.path.isdir(self._path):
          if download:
              _download_librispeech(root, url)
          else:
              raise RuntimeError(
                  f"Dataset not found at {self._path}. Please set `download=True` to download the dataset."
              )
  
      self._walker = sorted(
          str(p.stem)
          for p in Path(self._path).glob("*/*/*" + self._ext_audio)
      )
      self.blist = blist
  
file_path: audio/src/torchaudio/datasets/librispeech_biasing.py
imports: import os
  from pathlib import Path
  from typing import List, 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 1 to create a new LibriSpeechBiasing object from inherited base classes: Dataset with example: obj = LibriSpeechBiasing(root, url, folder_in_archive, download, blist)