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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) | |