Source code for torchaudio.datasets.librispeech
import os
from typing import Tuple, Union
from pathlib import Path
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio.datasets.utils import (
download_url,
extract_archive,
)
URL = "train-clean-100"
FOLDER_IN_ARCHIVE = "LibriSpeech"
_CHECKSUMS = {
"http://www.openslr.org/resources/12/dev-clean.tar.gz":
"76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3",
"http://www.openslr.org/resources/12/dev-other.tar.gz":
"12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365",
"http://www.openslr.org/resources/12/test-clean.tar.gz":
"39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23",
"http://www.openslr.org/resources/12/test-other.tar.gz":
"d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29",
"http://www.openslr.org/resources/12/train-clean-100.tar.gz":
"d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2",
"http://www.openslr.org/resources/12/train-clean-360.tar.gz":
"146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf",
"http://www.openslr.org/resources/12/train-other-500.tar.gz":
"ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2"
}
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("-")
file_text = speaker_id + "-" + chapter_id + ext_txt
file_text = os.path.join(path, speaker_id, chapter_id, file_text)
fileid_audio = speaker_id + "-" + chapter_id + "-" + utterance_id
file_audio = fileid_audio + ext_audio
file_audio = os.path.join(path, speaker_id, chapter_id, file_audio)
# Load audio
waveform, sample_rate = torchaudio.load(file_audio)
# Load text
with open(file_text) as ft:
for line in ft:
fileid_text, transcript = line.strip().split(" ", 1)
if fileid_audio == fileid_text:
break
else:
# Translation not found
raise FileNotFoundError("Translation not found for " + fileid_audio)
return (
waveform,
sample_rate,
transcript,
int(speaker_id),
int(chapter_id),
int(utterance_id),
)
[docs]class LIBRISPEECH(Dataset):
"""Create a Dataset for LibriSpeech.
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 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/12/"
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(url, root, hash_value=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, 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)