Source code for torchaudio.datasets.librispeech

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

import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from import Dataset
from torchaudio.datasets.utils import extract_archive

URL = "train-clean-100"
    "": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3",  # noqa: E501
    "": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365",  # noqa: E501
    "": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23",  # noqa: E501
    "": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29",  # noqa: E501
    "": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2",  # noqa: E501
    "": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf",  # noqa: E501
    "": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2",  # noqa: E501

def download_librispeech(root, url):
    base_url = ""
    ext_archive = ".tar.gz"

    filename = url + ext_archive
    archive = os.path.join(root, filename)
    download_url = os.path.join(base_url, filename)
    if not os.path.isfile(archive):
        checksum = _CHECKSUMS.get(download_url, None)
        download_url_to_file(download_url, archive, hash_prefix=checksum)

def load_librispeech_item(
    fileid: str, path: str, ext_audio: str, ext_txt: str
) -> Tuple[Tensor, int, str, int, int, int]:
    speaker_id, chapter_id, utterance_id = fileid.split("-")

    # Load audio
    fileid_audio = f"{speaker_id}-{chapter_id}-{utterance_id}"
    file_audio = fileid_audio + ext_audio
    file_audio = os.path.join(path, speaker_id, chapter_id, file_audio)
    waveform, sample_rate = torchaudio.load(file_audio)

    # Load text
    file_text = f"{speaker_id}-{chapter_id}{ext_txt}"
    file_text = os.path.join(path, 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:
            # Translation not found
            raise FileNotFoundError(f"Translation not found for {fileid_audio}")

    return (

[docs]class LIBRISPEECH(Dataset): """Create a Dataset for *LibriSpeech* [:footcite:`7178964`]. 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``). """ _ext_txt = ".trans.txt" _ext_audio = ".flac" def __init__( self, root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, ) -> None: if url not in _DATA_SUBSETS: raise ValueError(f"Invalid url '{url}' given; please provide one of {_DATA_SUBSETS}.") root = os.fspath(root) 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))
[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: (Tensor, int, str, int, int, int): ``(waveform, sample_rate, transcript, speaker_id, chapter_id, utterance_id)`` """ fileid = self._walker[n] return load_librispeech_item(fileid, self._path, self._ext_audio, self._ext_txt)
def __len__(self) -> int: return len(self._walker)


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