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 URL = "train-clean-100" FOLDER_IN_ARCHIVE = "LibriSpeech" SAMPLE_RATE = 16000 _DATA_SUBSETS = [ "dev-clean", "dev-other", "test-clean", "test-other", "train-clean-100", "train-clean-360", "train-other-500", ] _CHECKSUMS = { "http://www.openslr.org/resources/12/dev-clean.tar.gz": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3", # noqa: E501 "http://www.openslr.org/resources/12/dev-other.tar.gz": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365", # noqa: E501 "http://www.openslr.org/resources/12/test-clean.tar.gz": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23", # noqa: E501 "http://www.openslr.org/resources/12/test-other.tar.gz": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29", # noqa: E501 "http://www.openslr.org/resources/12/train-clean-100.tar.gz": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2", # noqa: E501 "http://www.openslr.org/resources/12/train-clean-360.tar.gz": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf", # noqa: E501 "http://www.openslr.org/resources/12/train-other-500.tar.gz": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2", # noqa: E501 } def _download_librispeech(root, url): base_url = "http://www.openslr.org/resources/12/" 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) _extract_tar(archive) def _get_librispeech_metadata( fileid: str, root: str, folder: str, ext_audio: str, ext_txt: str, blist: List[str] ) -> Tuple[str, int, str, int, int, int]: blist = blist or [] speaker_id, chapter_id, utterance_id = fileid.split("-") # Get audio path and sample rate fileid_audio = f"{speaker_id}-{chapter_id}-{utterance_id}" filepath = os.path.join(folder, speaker_id, chapter_id, f"{fileid_audio}{ext_audio}") # Load text file_text = f"{speaker_id}-{chapter_id}{ext_txt}" file_text = os.path.join(root, folder, speaker_id, chapter_id, file_text) uttblist = [] with open(file_text) as ft: for line in ft: fileid_text, transcript = line.strip().split(" ", 1) if fileid_audio == fileid_text: # get utterance biasing list for word in transcript.split(): if word in blist and word not in uttblist: uttblist.append(word) break else: # Translation not found raise FileNotFoundError(f"Translation not found for {fileid_audio}") return ( filepath, SAMPLE_RATE, transcript, int(speaker_id), int(chapter_id), int(utterance_id), uttblist, ) class LibriSpeechBiasing(Dataset): """*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: ``[]``). """ _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, 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 def get_metadata(self, n: int) -> Tuple[str, int, str, int, int, int]: """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform, but otherwise returns the same fields as :py:func:`__getitem__`. Args: n (int): The index of the sample to be loaded Returns: Tuple of the following items; str: Path to audio int: Sample rate str: Transcript int: Speaker ID int: Chapter ID int: Utterance ID list: List of biasing words in the utterance """ fileid = self._walker[n] return _get_librispeech_metadata(fileid, self._archive, self._url, self._ext_audio, self._ext_txt, self.blist) 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 list: List of biasing words in the utterance """ metadata = self.get_metadata(n) waveform = _load_waveform(self._archive, metadata[0], metadata[1]) return (waveform,) + metadata[1:] def __len__(self) -> int: return len(self._walker)