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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_zip, _load_waveform
SAMPLE_RATE = 16000
_ARCHIVE_CONFIGS = {
"dev": {
"archive_name": "vox1_dev_wav.zip",
"urls": [
"https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa",
"https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partab",
"https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partac",
"https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partad",
],
"checksums": [
"21ec6ca843659ebc2fdbe04b530baa4f191ad4b0971912672d92c158f32226a0",
"311d21e0c8cbf33573a4fce6c80e5a279d80736274b381c394319fc557159a04",
"92b64465f2b2a3dc0e4196ae8dd6828cbe9ddd1f089419a11e4cbfe2e1750df0",
"00e6190c770b27f27d2a3dd26ee15596b17066b715ac111906861a7d09a211a5",
],
},
"test": {
"archive_name": "vox1_test_wav.zip",
"url": "https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_test_wav.zip",
"checksum": "8de57f347fe22b2c24526e9f444f689ecf5096fc2a92018cf420ff6b5b15eaea",
},
}
_IDEN_SPLIT_URL = "https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/iden_split.txt"
_VERI_TEST_URL = "https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test.txt"
def _download_extract_wavs(root: str):
for archive in ["dev", "test"]:
archive_name = _ARCHIVE_CONFIGS[archive]["archive_name"]
archive_path = os.path.join(root, archive_name)
# The zip file of dev data is splited to 4 chunks.
# Download and combine them into one file before extraction.
if archive == "dev":
urls = _ARCHIVE_CONFIGS[archive]["urls"]
checksums = _ARCHIVE_CONFIGS[archive]["checksums"]
with open(archive_path, "wb") as f:
for url, checksum in zip(urls, checksums):
file_path = os.path.join(root, os.path.basename(url))
download_url_to_file(url, file_path, hash_prefix=checksum)
with open(file_path, "rb") as f_split:
f.write(f_split.read())
else:
url = _ARCHIVE_CONFIGS[archive]["url"]
checksum = _ARCHIVE_CONFIGS[archive]["checksum"]
download_url_to_file(url, archive_path, hash_prefix=checksum)
_extract_zip(archive_path)
def _get_flist(root: str, file_path: str, subset: str) -> List[str]:
f_list = []
if subset == "train":
index = 1
elif subset == "dev":
index = 2
else:
index = 3
with open(file_path, "r") as f:
for line in f:
id, path = line.split()
if int(id) == index:
f_list.append(path)
return sorted(f_list)
def _get_paired_flist(root: str, veri_test_path: str):
f_list = []
with open(veri_test_path, "r") as f:
for line in f:
label, path1, path2 = line.split()
f_list.append((label, path1, path2))
return f_list
def _get_file_id(file_path: str, _ext_audio: str):
speaker_id, youtube_id, utterance_id = file_path.split("/")[-3:]
utterance_id = utterance_id.replace(_ext_audio, "")
file_id = "-".join([speaker_id, youtube_id, utterance_id])
return file_id
class VoxCeleb1(Dataset):
"""*VoxCeleb1* :cite:`nagrani2017voxceleb` dataset.
Args:
root (str or Path): Path to the directory where the dataset is found or downloaded.
download (bool, optional):
Whether to download the dataset if it is not found at root path. (Default: ``False``).
"""
_ext_audio = ".wav"
def __init__(self, root: Union[str, Path], download: bool = False) -> None:
# Get string representation of 'root' in case Path object is passed
root = os.fspath(root)
self._path = os.path.join(root, "wav")
if not os.path.isdir(self._path):
if not download:
raise RuntimeError(
f"Dataset not found at {self._path}. Please set `download=True` to download the dataset."
)
_download_extract_wavs(root)
def get_metadata(self, n: int):
raise NotImplementedError
def __getitem__(self, n: int):
raise NotImplementedError
def __len__(self) -> int:
raise NotImplementedError
class VoxCeleb1Identification(VoxCeleb1):
"""*VoxCeleb1* :cite:`nagrani2017voxceleb` dataset for speaker identification task.
Each data sample contains the waveform, sample rate, speaker id, and the file id.
Args:
root (str or Path): Path to the directory where the dataset is found or downloaded.
subset (str, optional): Subset of the dataset to use. Options: ["train", "dev", "test"]. (Default: ``"train"``)
meta_url (str, optional): The url of meta file that contains the list of subset labels and file paths.
The format of each row is ``subset file_path". For example: ``1 id10006/nLEBBc9oIFs/00003.wav``.
``1``, ``2``, ``3`` mean ``train``, ``dev``, and ``test`` subest, respectively.
(Default: ``"https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/iden_split.txt"``)
download (bool, optional):
Whether to download the dataset if it is not found at root path. (Default: ``False``).
Note:
The file structure of `VoxCeleb1Identification` dataset is as follows:
└─ root/
└─ wav/
└─ speaker_id folders
Users who pre-downloaded the ``"vox1_dev_wav.zip"`` and ``"vox1_test_wav.zip"`` files need to move
the extracted files into the same ``root`` directory.
"""
def __init__(
self, root: Union[str, Path], subset: str = "train", meta_url: str = _IDEN_SPLIT_URL, download: bool = False
) -> None:
super().__init__(root, download)
if subset not in ["train", "dev", "test"]:
raise ValueError("`subset` must be one of ['train', 'dev', 'test']")
# download the iden_split.txt to get the train, dev, test lists.
meta_list_path = os.path.join(root, os.path.basename(meta_url))
if not os.path.exists(meta_list_path):
download_url_to_file(meta_url, meta_list_path)
self._flist = _get_flist(self._path, meta_list_path, subset)
def get_metadata(self, n: int) -> Tuple[str, int, int, str]:
"""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
Returns:
Tuple of the following items;
str:
Path to audio
int:
Sample rate
int:
Speaker ID
str:
File ID
"""
file_path = self._flist[n]
file_id = _get_file_id(file_path, self._ext_audio)
speaker_id = file_id.split("-")[0]
speaker_id = int(speaker_id[3:])
return file_path, SAMPLE_RATE, speaker_id, file_id
def __getitem__(self, n: int) -> Tuple[Tensor, int, int, str]:
"""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
int:
Speaker ID
str:
File ID
"""
metadata = self.get_metadata(n)
waveform = _load_waveform(self._path, metadata[0], metadata[1])
return (waveform,) + metadata[1:]
def __len__(self) -> int:
return len(self._flist)
class VoxCeleb1Verification(VoxCeleb1):
"""*VoxCeleb1* :cite:`nagrani2017voxceleb` dataset for speaker verification task.
Each data sample contains a pair of waveforms, sample rate, the label indicating if they are
from the same speaker, and the file ids.
Args:
root (str or Path): Path to the directory where the dataset is found or downloaded.
meta_url (str, optional): The url of meta file that contains a list of utterance pairs
and the corresponding labels. The format of each row is ``label file_path1 file_path2".
For example: ``1 id10270/x6uYqmx31kE/00001.wav id10270/8jEAjG6SegY/00008.wav``.
``1`` means the two utterances are from the same speaker, ``0`` means not.
(Default: ``"https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test.txt"``)
download (bool, optional):
Whether to download the dataset if it is not found at root path. (Default: ``False``).
Note:
The file structure of `VoxCeleb1Verification` dataset is as follows:
└─ root/
└─ wav/
└─ speaker_id folders
Users who pre-downloaded the ``"vox1_dev_wav.zip"`` and ``"vox1_test_wav.zip"`` files need to move
the extracted files into the same ``root`` directory.
"""
def __init__(self, root: Union[str, Path], meta_url: str = _VERI_TEST_URL, download: bool = False) -> None:
super().__init__(root, download)
# download the veri_test.txt to get the list of training pairs and labels.
meta_list_path = os.path.join(root, os.path.basename(meta_url))
if not os.path.exists(meta_list_path):
download_url_to_file(meta_url, meta_list_path)
self._flist = _get_paired_flist(self._path, meta_list_path)
def get_metadata(self, n: int) -> Tuple[str, str, int, int, str, str]:
"""Get metadata for the n-th sample from the dataset. Returns filepaths instead of waveforms,
but otherwise returns the same fields as :py:func:`__getitem__`.
Args:
n (int): The index of the sample
Returns:
Tuple of the following items;
str:
Path to audio file of speaker 1
str:
Path to audio file of speaker 2
int:
Sample rate
int:
Label
str:
File ID of speaker 1
str:
File ID of speaker 2
"""
label, file_path_spk1, file_path_spk2 = self._flist[n]
label = int(label)
file_id_spk1 = _get_file_id(file_path_spk1, self._ext_audio)
file_id_spk2 = _get_file_id(file_path_spk2, self._ext_audio)
return file_path_spk1, file_path_spk2, SAMPLE_RATE, label, file_id_spk1, file_id_spk2
def __getitem__(self, n: int) -> Tuple[Tensor, Tensor, int, int, str, str]:
"""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 of speaker 1
Tensor:
Waveform of speaker 2
int:
Sample rate
int:
Label
str:
File ID of speaker 1
str:
File ID of speaker 2
"""
metadata = self.get_metadata(n)
waveform_spk1 = _load_waveform(self._path, metadata[0], metadata[2])
waveform_spk2 = _load_waveform(self._path, metadata[1], metadata[2])
return (waveform_spk1, waveform_spk2) + metadata[2:]
def __len__(self) -> int:
return len(self._flist)
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