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Source code for torchaudio.datasets.libritts

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 torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive

URL = "train-clean-100"
FOLDER_IN_ARCHIVE = "LibriTTS"
_CHECKSUMS = {
    "http://www.openslr.org/resources/60/dev-clean.tar.gz": "da0864e1bd26debed35da8a869dd5c04dfc27682921936de7cff9c8a254dbe1a",  # noqa: E501
    "http://www.openslr.org/resources/60/dev-other.tar.gz": "d413eda26f3a152ac7c9cf3658ef85504dfb1b625296e5fa83727f5186cca79c",  # noqa: E501
    "http://www.openslr.org/resources/60/test-clean.tar.gz": "234ea5b25859102a87024a4b9b86641f5b5aaaf1197335c95090cde04fe9a4f5",  # noqa: E501
    "http://www.openslr.org/resources/60/test-other.tar.gz": "33a5342094f3bba7ccc2e0500b9e72d558f72eb99328ac8debe1d9080402f10d",  # noqa: E501
    "http://www.openslr.org/resources/60/train-clean-100.tar.gz": "c5608bf1ef74bb621935382b8399c5cdd51cd3ee47cec51f00f885a64c6c7f6b",  # noqa: E501
    "http://www.openslr.org/resources/60/train-clean-360.tar.gz": "ce7cff44dcac46009d18379f37ef36551123a1dc4e5c8e4eb73ae57260de4886",  # noqa: E501
    "http://www.openslr.org/resources/60/train-other-500.tar.gz": "e35f7e34deeb2e2bdfe4403d88c8fdd5fbf64865cae41f027a185a6965f0a5df",  # noqa: E501
}


def load_libritts_item(
    fileid: str,
    path: str,
    ext_audio: str,
    ext_original_txt: str,
    ext_normalized_txt: str,
) -> Tuple[Tensor, int, str, str, int, int, str]:
    speaker_id, chapter_id, segment_id, utterance_id = fileid.split("_")
    utterance_id = fileid

    normalized_text = utterance_id + ext_normalized_txt
    normalized_text = os.path.join(path, speaker_id, chapter_id, normalized_text)

    original_text = utterance_id + ext_original_txt
    original_text = os.path.join(path, speaker_id, chapter_id, original_text)

    file_audio = utterance_id + ext_audio
    file_audio = os.path.join(path, speaker_id, chapter_id, file_audio)

    # Load audio
    waveform, sample_rate = torchaudio.load(file_audio)

    # Load original text
    with open(original_text) as ft:
        original_text = ft.readline()

    # Load normalized text
    with open(normalized_text, "r") as ft:
        normalized_text = ft.readline()

    return (
        waveform,
        sample_rate,
        original_text,
        normalized_text,
        int(speaker_id),
        int(chapter_id),
        utterance_id,
    )


[docs]class LIBRITTS(Dataset): """Create a Dataset for *LibriTTS* [:footcite:`Zen2019LibriTTSAC`]. 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``). """ _ext_original_txt = ".original.txt" _ext_normalized_txt = ".normalized.txt" _ext_audio = ".wav" 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_archive(archive) 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, str, int, int, str]: """Load the n-th sample from the dataset. Args: n (int): The index of the sample to be loaded Returns: (Tensor, int, str, str, str, int, int, str): ``(waveform, sample_rate, original_text, normalized_text, speaker_id, chapter_id, utterance_id)`` """ fileid = self._walker[n] return load_libritts_item( fileid, self._path, self._ext_audio, self._ext_original_txt, self._ext_normalized_txt, )
def __len__(self) -> int: return len(self._walker)

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