Source code for torchaudio.datasets.librilight_limited

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

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

_ARCHIVE_NAME = "librispeech_finetuning"
_URL = ""
_CHECKSUM = "5d1efdc777b548194d7e09ba89126e2188026df9fd57aa57eb14408d2b2342af"

def _get_fileids_paths(path, subset, _ext_audio) -> 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]
    if subset == "10min":
        files_paths = [
            (os.path.join(os.path.dirname(p), "..", ".."), str(p.stem))
            for p in Path(path).glob("1h/0/*/*/*/*" + _ext_audio)
    elif subset in ["1h", "10h"]:
        files_paths = [
            (os.path.join(os.path.dirname(p), "..", ".."), str(p.stem))
            for p in Path(path).glob("1h/*/*/*/*/*" + _ext_audio)
        if subset == "10h":
            files_paths += [
                (os.path.join(os.path.dirname(p), "..", ".."), str(p.stem))
                for p in Path(path).glob("9h/*/*/*/*" + _ext_audio)
        raise ValueError(f"Unsupported subset value. Found {subset}.")
    files_paths = sorted(files_paths, key=lambda x: x[0] + x[1])
    return files_paths

[docs]class LibriLightLimited(Dataset): """Create a Dataset for LibriLightLimited, which is the supervised subset of LibriLight dataset. 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: assert subset in ["10min", "1h", "10h"], "`subset` must be one of ['10min', '1h', '10h']" 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_archive(archive) self._fileids_paths = _get_fileids_paths(self._path, subset, 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)`` """ file_path, fileid = self._fileids_paths[n] return load_librispeech_item(fileid, file_path, self._ext_audio, self._ext_txt)
def __len__(self) -> int: return len(self._fileids_paths)


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