Datasets:

101arrowz
/

vox_celeb

	# coding=utf-8
	# Copyright 2022 The HuggingFace Datasets Authors and Arjun Barrett.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""VoxCeleb audio-visual human speech dataset."""

	import json
	import os
	from urllib.parse import urlparse, parse_qs
	from getpass import getpass
	from hashlib import sha256
	from itertools import repeat
	from multiprocessing import Manager, Pool, Process
	from pathlib import Path
	from shutil import copyfileobj
	from warnings import catch_warnings, filterwarnings
	from urllib3.exceptions import InsecureRequestWarning

	import pandas as pd
	import requests

	import datasets
	import fsspec as fs
	from io import BytesIO

	_CITATION = """\
	@Article{Nagrani19,
	author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman",
	title = "Voxceleb: Large-scale speaker verification in the wild",
	journal = "Computer Science and Language",
	year = "2019",
	publisher = "Elsevier",
	}

	@InProceedings{Chung18b,
	author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.",
	title = "VoxCeleb2: Deep Speaker Recognition",
	booktitle = "INTERSPEECH",
	year = "2018",
	}

	@InProceedings{Nagrani17,
	author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.",
	title = "VoxCeleb: a large-scale speaker identification dataset",
	booktitle = "INTERSPEECH",
	year = "2017",
	}
	"""

	_DESCRIPTION = """\
	VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube
	"""

	_URL = "https://mm.kaist.ac.kr/datasets/voxceleb"
	_REQ_URL = "https://cn01.mmai.io/keyreq/voxceleb"

	_URLS = {
	"video": {
	"placeholder": "https://cn01.mmai.io/download/voxceleb?file=vox2_dev_mp4",
	"dev": (
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partaa",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partab",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partac",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partad",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partae",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partaf",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partag",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partah",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_mp4_partai",
	),
	"test": "https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_test_mp4.zip",
	},
	"audio1": {
	"placeholder": "https://cn01.mmai.io/download/voxceleb?file=vox1_dev_wav",
	"dev": (
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox1_dev_wav_partaa",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox1_dev_wav_partab",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox1_dev_wav_partac",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox1_dev_wav_partad",
	),
	"test": "https://cn01.mmai.io/download/voxceleb?key={key}&file=vox1_test_wav.zip",
	},
	"audio2": {
	"placeholder": "https://cn01.mmai.io/download/voxceleb?file=vox2_dev_aac",
	"dev": (
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partaa",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partab",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partac",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partad",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partae",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partaf",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partag",
	"https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_dev_aac_partah",
	),
	"test": "https://cn01.mmai.io/download/voxceleb?key={key}&file=vox2_test_aac.zip",
	},
	}

	_NO_AUTH_URLS = {
	"video": {
	"dev": {
	1:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_mp4_1.zip",
	2:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_mp4_2.zip",
	3:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_mp4_3.zip",
	4:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_mp4_4.zip",
	5:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_mp4_5.zip",
	6:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_mp4_6.zip",

	},
	"test": {1:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_test_mp4.zip"}
	},
	"audio1": {
	"dev": {1:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav.zip"},
	"test": {1:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_test_wav.zip"},
	},
	"audio2": {
	"dev":
	{1:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_aac_1.zip",2:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_aac_2.zip"},
	"test": {1:"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_test_aac.zip"},
	},
	}

	_DATASET_IDS = {"video": "vox2", "audio1": "vox1", "audio2": "vox2"}

	_PLACEHOLDER_MAPS = dict(
	value
	for urls in _URLS.values()
	for value in ((urls["placeholder"], urls["dev"]), (urls["test"], (urls["test"],)))
	)

	def _mp_download(
	url,
	tmp_path,
	cred_key,
	resume_pos,
	length,
	queue,
	):
	if length == resume_pos:
	return
	with open(tmp_path, "ab" if resume_pos else "wb") as tmp:
	headers = {}
	if resume_pos != 0:
	headers["Range"] = f"bytes={resume_pos}-"
	with catch_warnings():
	filterwarnings("ignore", category=InsecureRequestWarning)
	response = requests.get(
	url.format(key=cred_key), headers=headers, verify=False, stream=True
	)
	if response.status_code >= 200 and response.status_code < 300:
	for chunk in response.iter_content(chunk_size=65536):
	queue.put(len(chunk))
	tmp.write(chunk)
	else:
	raise ConnectionError("failed to fetch dataset")


	class VoxCeleb(datasets.GeneratorBasedBuilder):
	"""VoxCeleb is an unlabled dataset consisting of short clips of human speech from interviews on YouTube"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="video", version=VERSION, description="Video clips of human speech"
	),
	datasets.BuilderConfig(
	name="audio", version=VERSION, description="Audio clips of human speech"
	),
	datasets.BuilderConfig(
	name="audio1",
	version=datasets.Version("1.0.0"),
	description="Audio clips of human speech from VoxCeleb1",
	),
	datasets.BuilderConfig(
	name="audio2",
	version=datasets.Version("2.0.0"),
	description="Audio clips of human speech from VoxCeleb2",
	),
	]

	def _info(self):
	features = {
	"file": datasets.Value("string"),
	"file_format": datasets.Value("string"),
	"dataset_id": datasets.Value("string"),
	"speaker_id": datasets.Value("string"),
	"speaker_gender": datasets.Value("string"),
	"video_id": datasets.Value("string"),
	"clip_index": datasets.Value("int32"),
	}
	if self.config.name.startswith("audio"):
	features["speaker_name"] = datasets.Value("string")
	features["speaker_nationality"] = datasets.Value("string")
	features["audio"] = datasets.Audio()
	if self.config.name.startswith("video"):
	features["video"] = datasets.Value("large_binary")

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	homepage=_URL,
	supervised_keys=datasets.info.SupervisedKeysData("file", "speaker_id"),
	features=datasets.Features(features),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	if dl_manager.is_streaming and self.config.name != "audio1" and self.config.name != "video":
	raise TypeError("Streaming is only supported for vox1 wav and vox2 mp4 features")
	targets = (
	["audio1", "audio2"] if self.config.name == "audio" else [self.config.name]
	)
	cred_key = os.environ.get("HUGGING_FACE_VOX_CELEB_KEY")
	hf_dir = os.getenv(
	"HF_HOME",
	os.path.join(
	os.getenv("XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache")),
	"huggingface"
	)
	)
	creds_path = Path(hf_dir) / f"voxceleb_{self.VERSION}_credentials"
	all_urls = _URLS

	if cred_key is None and not dl_manager.is_streaming:
	if creds_path.exists():
	with open(creds_path, "r") as creds:
	cred_key = json.load(creds)
	else:
	print(
	"You need a key to access VoxCeleb directly.",
	f"Fill out the form ({_REQ_URL}) and paste in any of the download links you receive in your email.",
	"Alternatively, enter an empty string to use a third-party proxy by https://huggingface.co/ProgramComputer."
	)
	cred_url = getpass("Paste any VoxCeleb download URL here (leave blank for proxy): ")
	if cred_url != "":
	cred_url_parsed = urlparse(cred_url)
	if cred_url_parsed.scheme != 'https' or cred_url_parsed.hostname != 'cn01.mmai.io' or cred_url_parsed.path != '/download/voxceleb':
	raise ValueError("couldn't parse as VoxCeleb download URL")
	cred_url_query = parse_qs(cred_url_parsed.query)
	if 'key' not in cred_url_query or len(cred_url_query['key']) != 1:
	raise ValueError("couldn't find key in URL")
	cred_key = cred_url_query['key'][0]

	if cred_key is None:
	all_urls = _NO_AUTH_URLS

	saved_credentials = False

	def save_credentials():
	nonlocal saved_credentials, cred_key, creds_path
	if not saved_credentials:
	creds_path.parent.mkdir(exist_ok=True)
	with open(creds_path, "w") as creds:
	json.dump(cred_key, creds)
	saved_credentials = True

	def download_custom(placeholder_url, path):
	nonlocal dl_manager, cred_key
	sources = _PLACEHOLDER_MAPS[placeholder_url]
	tmp_paths = []
	lengths = []
	start_positions = []
	for url in sources:
	with catch_warnings():
	filterwarnings("ignore", category=InsecureRequestWarning)
	head = requests.get(url.format(key=cred_key), verify=False, timeout=5, stream=True)
	try:
	if head.status_code == 401:
	raise ValueError("failed to authenticate with VoxCeleb host")
	if head.status_code < 200 or head.status_code >= 300:
	raise ValueError("failed to fetch dataset")
	save_credentials()
	content_length = head.headers.get("Content-Length")
	if content_length is None:
	raise ValueError("expected non-empty Content-Length")
	content_length = int(content_length)
	finally:
	head.close()
	tmp_path = Path(path + "." + sha256(url.encode("utf-8")).hexdigest())
	tmp_paths.append(tmp_path)
	lengths.append(content_length)
	start_positions.append(
	tmp_path.stat().st_size
	if tmp_path.exists() and dl_manager.download_config.resume_download
	else 0
	)

	def progress(q, cur, total):
	with datasets.utils.logging.tqdm(
	unit="B",
	unit_scale=True,
	total=total,
	initial=cur,
	desc="Downloading",
	disable=not datasets.utils.logging.is_progress_bar_enabled(),
	) as progress:
	while cur < total:
	try:
	added = q.get(timeout=1)
	progress.update(added)
	cur += added
	except:
	continue

	manager = Manager()
	q = manager.Queue()
	with Pool(len(sources)) as pool:
	proc = Process(
	target=progress,
	args=(q, sum(start_positions), sum(lengths)),
	daemon=True,
	)
	proc.start()
	pool.starmap(
	_mp_download,
	zip(
	sources,
	tmp_paths,
	repeat(cred_key),
	start_positions,
	lengths,
	repeat(q),
	),
	)
	pool.close()
	proc.join()
	with open(path, "wb") as out:
	for tmp_path in tmp_paths:
	with open(tmp_path, "rb") as tmp:
	copyfileobj(tmp, out)
	tmp_path.unlink()

	metadata = dl_manager.download(
	dict(
	(
	target,
	f"https://mm.kaist.ac.kr/datasets/voxceleb/meta/{_DATASET_IDS[target]}_meta.csv",
	)
	for target in targets
	)
	)
	target_dict = dict(
	(
	placeholder_key,
	dict(
	(target, all_urls[target][placeholder_key])
	for target in targets
	),
	)
	for placeholder_key in ("dev" if cred_key is None else "placeholder", "test")
	)
	download_paths = dl_manager.download_custom(
	target_dict,
	download_custom,
	) if cred_key is not None else dl_manager.download(
	target_dict
	)

	mapped_paths = dl_manager.extract(
	target_dict
	)


	apply_function_recursive = lambda d, f: {k: apply_function_recursive(v, f) if isinstance(v, dict) else f(v) for k, v in d.items()}

	mapped_paths = apply_function_recursive(mapped_paths, dl_manager.iter_files)

	return [
	datasets.SplitGenerator(
	name="train",
	gen_kwargs={
	"paths": mapped_paths["placeholder"] if not dl_manager.is_streaming else mapped_paths["dev"] ,
	"meta_paths": metadata,
	},
	),
	datasets.SplitGenerator(
	name="test",
	gen_kwargs={
	"paths": mapped_paths["test"],
	"meta_paths": metadata,
	},
	),
	]

	def _generate_examples(self,paths, meta_paths):
	key = 0
	cred_key = os.environ.get("HUGGING_FACE_VOX_CELEB_KEY")
	for conf in paths:
	dataset_id = "vox1" if conf == "audio1" else "vox2"
	meta = pd.read_csv(
	meta_paths[conf],
	sep="\t" if conf == "audio1" else " ,",
	index_col=0,
	engine="python",
	)
	if cred_key is None:
	for path in paths[conf].values():
	for file in path:
	try:
	t = tuple(file.split("::")[0].split("/")[2:])
	_,dataset_format,speaker,video_id,clip_index= (None,) * (5 - len(t)) + t
	except Exception:
	raise Exception(file.split("::")[0].split("/")[2:])
	speaker_info = meta.loc[speaker]
	clip_index = int(Path(clip_index).stem)
	info = {
	"file": file,
	"file_format": dataset_format,
	"dataset_id": dataset_id,
	"speaker_id": speaker,
	"speaker_gender": speaker_info["Gender"],
	"video_id": video_id,
	"clip_index": clip_index,
	}
	if dataset_id == "vox1":
	info["speaker_name"] = speaker_info["VGGFace1 ID"]
	info["speaker_nationality"] = speaker_info["Nationality"]
	if conf.startswith("audio"):
	info["audio"] = info["file"]
	if conf.startswith("video"):
	with fs.open(info["file"], 'rb') as f:
	info["video"] = BytesIO(f.read()).getvalue()
	yield key, info
	key += 1
	else:
	dataset_path = next(Path(paths[conf]).iterdir())
	dataset_format = dataset_path.name
	for speaker_path in dataset_path.iterdir():
	speaker = speaker_path.name
	speaker_info = meta.loc[speaker]
	for video in speaker_path.iterdir():
	video_id = video.name
	for clip in video.iterdir():
	clip_index = int(clip.stem)
	clip = os.path.join(local_extracted_archive, clip.name) if local_extracted_archive else clip
	info = {
	"file": str(clip),
	"file_format": dataset_format,
	"dataset_id": dataset_id,
	"speaker_id": speaker,
	"speaker_gender": speaker_info["Gender"],
	"video_id": video_id,
	"clip_index": clip_index,
	}
	if dataset_id == "vox1":
	info["speaker_name"] = speaker_info["VGGFace1 ID"]
	info["speaker_nationality"] = speaker_info["Nationality"]
	if conf.startswith("audio"):
	info["audio"] = info["file"]
	if conf.startswith("video"):
	with fs.open(info["file"], 'rb') as f:
	info["video"] = BytesIO(f.read()).getvalue()
	yield key, info
	key += 1