Source code for torchaudio.datasets.librilight_limited

import os
from pathlib import Path
from typing import List, Tuple, Union

import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from import Dataset
from torchaudio.datasets.librispeech import _get_librispeech_metadata
from torchaudio.datasets.utils import _extract_tar

_ARCHIVE_NAME = "librispeech_finetuning"
_URL = ""
_CHECKSUM = "5d1efdc777b548194d7e09ba89126e2188026df9fd57aa57eb14408d2b2342af"
_SUBSET_MAP = {"10min": ["1h/0"], "1h": ["1h/*"], "10h": ["1h/*", "9h"]}

def _get_fileids_paths(path: Path, folders: List[str], _ext_audio: str) -> List[Tuple[str, str]]:
    """Get the file names and the corresponding file paths without `speaker_id`
    and `chapter_id` directories.
    The format of path is like:
        {root}/{_ARCHIVE_NAME}/1h/[0-5]/[clean, other] or
        {root}/{_ARCHIVE_NAME}/9h/[clean, other]

        path (Path): Root path to the dataset.
        folders (List[str]): Folders that contain the desired audio files.
        _ext_audio (str): Extension of audio files.

        List[Tuple[str, str]]:
            List of tuples where the first element is the relative path to the audio file.
            The format of relative path is like:
            1h/[0-5]/[clean, other] or 9h/[clean, other]
            The second element is the file name without audio extension.

    path = Path(path)
    files_paths = []
    for folder in folders:
        paths = [p.relative_to(path) for p in path.glob(f"{folder}/*/*/*/*{_ext_audio}")]
        files_paths += [(str(p.parent.parent.parent), str(p.stem)) for p in paths]  # get subset folder and file name
    files_paths.sort(key=lambda x: x[0] + x[1])
    return files_paths

[docs]class LibriLightLimited(Dataset): """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``). """ _ext_txt = ".trans.txt" _ext_audio = ".flac" def __init__( self, root: Union[str, Path], subset: str = "10min", download: bool = False, ) -> None: if subset not in _SUBSET_MAP: raise ValueError(f"`subset` must be one of {_SUBSET_MAP.keys()}. Found: {subset}") folders = _SUBSET_MAP[subset] root = os.fspath(root) self._path = os.path.join(root, _ARCHIVE_NAME) archive = os.path.join(root, f"{_ARCHIVE_NAME}.tgz") if not os.path.isdir(self._path): if not download: raise RuntimeError("Dataset not found. Please use `download=True` to download") if not os.path.isfile(archive): download_url_to_file(_URL, archive, hash_prefix=_CHECKSUM) _extract_tar(archive) self._fileids_paths = _get_fileids_paths(self._path, folders, self._ext_audio)
[docs] 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:]
def __len__(self) -> int: return len(self._fileids_paths)


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