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"""VoxCeleb audio-visual human speech dataset.""" |
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import json |
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import os |
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from getpass import getpass |
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from hashlib import sha256 |
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from itertools import repeat |
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from multiprocessing import Manager, Pool, Process |
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from pathlib import Path |
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from shutil import copyfileobj |
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import pandas as pd |
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import requests |
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import datasets |
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import urllib3 |
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) |
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_CITATION = """\ |
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@Article{Nagrani19, |
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author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman", |
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title = "Voxceleb: Large-scale speaker verification in the wild", |
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journal = "Computer Science and Language", |
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year = "2019", |
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publisher = "Elsevier", |
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} |
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@InProceedings{Chung18b, |
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author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.", |
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title = "VoxCeleb2: Deep Speaker Recognition", |
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booktitle = "INTERSPEECH", |
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year = "2018", |
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} |
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@InProceedings{Nagrani17, |
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author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.", |
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title = "VoxCeleb: a large-scale speaker identification dataset", |
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booktitle = "INTERSPEECH", |
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year = "2017", |
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} |
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""" |
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_DESCRIPTION = """\ |
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VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube |
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""" |
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_URL = "https://mm.kaist.ac.kr/datasets/voxceleb" |
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_URLS = { |
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"video": { |
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"placeholder": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_parta", |
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"dev": ( |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partaa", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partab", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partac", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partad", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partae", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partaf", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partag", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partah", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partai", |
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), |
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"test": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_test_mp4.zip", |
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}, |
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"audio1": { |
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"placeholder": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_parta", |
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"dev": ( |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partaa", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partab", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partac", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partad", |
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), |
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"test": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_test_wav.zip", |
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}, |
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"audio2": { |
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"placeholder": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_parta", |
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"dev": ( |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partaa", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partab", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partac", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partad", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partae", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partaf", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partag", |
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"https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partah", |
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), |
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"test": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_test_aac.zip", |
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}, |
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} |
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_DATASET_IDS = {"video": "vox2", "audio1": "vox1", "audio2": "vox2"} |
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_PLACEHOLDER_MAPS = dict( |
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value |
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for urls in _URLS.values() |
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for value in ((urls["placeholder"], urls["dev"]), (urls["test"], (urls["test"],))) |
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) |
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def _mp_download( |
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url, |
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tmp_path, |
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resume_pos, |
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length, |
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queue, |
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): |
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if length == resume_pos: |
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return |
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with open(tmp_path, "ab" if resume_pos else "wb") as tmp: |
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headers = {} |
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if resume_pos != 0: |
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headers["Range"] = f"bytes={resume_pos}-" |
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response = requests.get( |
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url, headers=headers, stream=True |
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) |
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if response.status_code >= 200 and response.status_code < 300: |
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for chunk in response.iter_content(chunk_size=65536): |
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queue.put(len(chunk)) |
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tmp.write(chunk) |
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else: |
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raise ConnectionError("failed to fetch dataset") |
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class VoxCeleb(datasets.GeneratorBasedBuilder): |
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"""VoxCeleb is an unlabled dataset consisting of short clips of human speech from interviews on YouTube""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="video", version=VERSION, description="Video clips of human speech" |
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), |
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datasets.BuilderConfig( |
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name="audio", version=VERSION, description="Audio clips of human speech" |
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), |
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datasets.BuilderConfig( |
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name="audio1", |
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version=datasets.Version("1.0.0"), |
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description="Audio clips of human speech from VoxCeleb1", |
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), |
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datasets.BuilderConfig( |
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name="audio2", |
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version=datasets.Version("2.0.0"), |
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description="Audio clips of human speech from VoxCeleb2", |
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), |
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] |
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def _info(self): |
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features = { |
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"file": datasets.Value("string"), |
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"file_format": datasets.Value("string"), |
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"dataset_id": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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"speaker_gender": datasets.Value("string"), |
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"video_id": datasets.Value("string"), |
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"clip_index": datasets.Value("int32"), |
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} |
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if self.config.name == "audio1": |
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features["speaker_name"] = datasets.Value("string") |
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features["speaker_nationality"] = datasets.Value("string") |
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if self.config.name.startswith("audio"): |
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features["audio"] = datasets.Audio(sampling_rate=16000) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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homepage=_URL, |
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supervised_keys=datasets.info.SupervisedKeysData("file", "speaker_id"), |
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features=datasets.Features(features), |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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if dl_manager.is_streaming: |
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raise TypeError("Streaming is not supported for VoxCeleb") |
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targets = ( |
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["audio1", "audio2"] if self.config.name == "audio" else [self.config.name] |
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) |
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def download_custom(placeholder_url, path): |
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nonlocal dl_manager |
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sources = _PLACEHOLDER_MAPS[placeholder_url] |
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tmp_paths = [] |
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lengths = [] |
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start_positions = [] |
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for url in sources: |
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head = requests.head(url,timeout=5,stream=True,allow_redirects=True,verify=False) |
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if head.status_code == 401: |
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raise ValueError("failed to authenticate with VoxCeleb host") |
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if head.status_code < 200 or head.status_code >= 300: |
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raise ValueError("failed to fetch dataset") |
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content_length = head.headers.get("Content-Length") |
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if content_length is None: |
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raise ValueError("expected non-empty Content-Length") |
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content_length = int(content_length) |
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tmp_path = Path(path + "." + sha256(url.encode("utf-8")).hexdigest()) |
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tmp_paths.append(tmp_path) |
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lengths.append(content_length) |
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start_positions.append( |
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tmp_path.stat().st_size |
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if tmp_path.exists() and dl_manager.download_config.resume_download |
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else 0 |
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) |
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def progress(q, cur, total): |
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with datasets.utils.logging.tqdm( |
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unit="B", |
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unit_scale=True, |
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total=total, |
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initial=cur, |
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desc="Downloading", |
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disable=not datasets.utils.logging.is_progress_bar_enabled(), |
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) as progress: |
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while cur < total: |
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try: |
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added = q.get(timeout=1) |
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progress.update(added) |
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cur += added |
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except: |
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continue |
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manager = Manager() |
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q = manager.Queue() |
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with Pool(len(sources)) as pool: |
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proc = Process( |
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target=progress, |
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args=(q, sum(start_positions), sum(lengths)), |
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daemon=True, |
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) |
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proc.start() |
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pool.starmap( |
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_mp_download, |
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zip( |
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sources, |
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tmp_paths, |
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start_positions, |
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lengths, |
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repeat(q), |
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), |
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) |
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pool.close() |
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proc.join() |
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with open(path, "wb") as out: |
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for tmp_path in tmp_paths: |
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with open(tmp_path, "rb") as tmp: |
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copyfileobj(tmp, out) |
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tmp_path.unlink() |
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metadata = dl_manager.download( |
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dict( |
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( |
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target, |
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f"https://mm.kaist.ac.kr/datasets/voxceleb/meta/{_DATASET_IDS[target]}_meta.csv", |
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) |
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for target in targets |
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) |
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) |
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mapped_paths = dl_manager.extract( |
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dl_manager.download_custom( |
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dict( |
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( |
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placeholder_key, |
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dict( |
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(target, _URLS[target][placeholder_key]) |
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for target in targets |
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), |
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) |
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for placeholder_key in ("placeholder", "test") |
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), |
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download_custom, |
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) |
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) |
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return [ |
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datasets.SplitGenerator( |
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name="train", |
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gen_kwargs={ |
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"paths": mapped_paths["placeholder"], |
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"meta_paths": metadata, |
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}, |
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), |
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datasets.SplitGenerator( |
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name="test", |
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gen_kwargs={ |
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"paths": mapped_paths["test"], |
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"meta_paths": metadata, |
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}, |
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), |
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] |
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def _generate_examples(self, paths, meta_paths): |
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key = 0 |
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for conf in paths: |
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dataset_id = "vox1" if conf == "audio1" else "vox2" |
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meta = pd.read_csv( |
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meta_paths[conf], |
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sep="\t" if conf == "audio1" else " ,", |
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index_col=0, |
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engine="python", |
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) |
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dataset_path = next(Path(paths[conf]).iterdir()) |
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dataset_format = dataset_path.name |
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for speaker_path in dataset_path.iterdir(): |
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speaker = speaker_path.name |
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speaker_info = meta.loc[speaker] |
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for video in speaker_path.iterdir(): |
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video_id = video.name |
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for clip in video.iterdir(): |
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clip_index = int(clip.stem) |
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info = { |
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"file": str(clip), |
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"file_format": dataset_format, |
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"dataset_id": dataset_id, |
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"speaker_id": speaker, |
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"speaker_gender": speaker_info["Gender"], |
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"video_id": video_id, |
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"clip_index": clip_index, |
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} |
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if dataset_id == "vox1": |
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info["speaker_name"] = speaker_info["VGGFace1 ID"] |
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info["speaker_nationality"] = speaker_info["Nationality"] |
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if conf.startswith("audio"): |
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info["audio"] = info["file"] |
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yield key, info |
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key += 1 |
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