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# 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 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
import numpy as np
import pandas as pd
import requests
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, TypeVar, Union
import warnings
import datasets
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
_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"
_URLS = {
"video": {
"placeholder": "hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partaa",
"dev": (
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partaa",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partab",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partac",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partad",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partae",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partaf",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partag",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partah",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_mp4_partai",
),
"test": "hf://datasets/ProgramComputer/voxceleb/vox2/vox2_test_mp4.zip",
},
"audio1": {
"placeholder": "hf://datasets/ProgramComputer/voxceleb/vox1/vox1_dev_wav_partaa",
"dev": (
"hf://datasets/ProgramComputer/voxceleb/vox1/vox1_dev_wav_partaa",
"hf://datasets/ProgramComputer/voxceleb/vox1/vox1_dev_wav_partab",
"hf://datasets/ProgramComputer/voxceleb/vox1/vox1_dev_wav_partac",
"hf://datasets/ProgramComputer/voxceleb/vox1/vox1_dev_wav_partad",
),
"test": "hf://datasets/ProgramComputer/voxceleb/vox1/vox1_test_wav.zip",
},
"audio2": {
"placeholder": "hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partaa",
"dev": (
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partaa",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partab",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partac",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partad",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partae",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partaf",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partag",
"hf://datasets/ProgramComputer/voxceleb/vox2/vox2_dev_aac_partah",
),
"test": "hf://datasets/ProgramComputer/voxceleb/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"],)))
)
class NestedDataStructure:
def __init__(self, data=None):
self.data = data if data is not None else []
def flatten(self, data=None):
data = data if data is not None else self.data
if isinstance(data, dict):
return self.flatten(list(data.values()))
elif isinstance(data, (list, tuple)):
return [flattened for item in data for flattened in self.flatten(item)]
else:
return [data]
def _mp_download(
url,
tmp_path,
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}-"
response = requests.get(
url, headers=headers, 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 Test(datasets.GeneratorBasedBuilder):
"""VoxCeleb is an unlabled dataset consisting of short clips of human speech from interviews on YouTube"""
VERSION = datasets.Version("1.0.0")
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 == "audio1":
features["speaker_name"] = datasets.Value("string")
features["speaker_nationality"] = datasets.Value("string")
if self.config.name.startswith("audio"):
features["audio"] = datasets.Audio(sampling_rate=16000)
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):
targets = (
["audio1", "audio2"] if self.config.name == "audio" else [self.config.name]
)
def download_custom(placeholder_url, path):
nonlocal dl_manager
sources = _PLACEHOLDER_MAPS[placeholder_url]
tmp_paths = []
lengths = []
start_positions = []
for url in sources:
head = requests.head(url,timeout=5,stream=True,allow_redirects=True,verify=False)
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")
content_length = head.headers.get("Content-Length")
if content_length is None:
raise ValueError("expected non-empty Content-Length")
content_length = int(content_length)
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,
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
)
)
tmp_paths = dl_manager.download(
dict( (
placeholder_key,
dict(
(target, _URLS[target][placeholder_key])
for target in targets
),
)
for placeholder_key in ("dev",)
)
)
raise Exception("HELLO BEFORE")
mapped_paths = {"dev":{}}
for key,value in tmp_paths["dev"].items():
with open(value[0], "wb") as out:
mapped_paths['dev'][key] = (value[0])
for tmp_path in value[1:]:
with open(tmp_path, "rb") as tmp:
copyfileobj(tmp, out)
#tmp_path.unlink()
raise Exception("HELLO")
mapped_paths = mapped_paths.append(dl_manager.extract( dl_manager.download(
dict( (
placeholder_key,
dict(
(target, _URLS[target][placeholder_key])
for target in targets
),
)
for placeholder_key in ("test",)
)
)))
raise Exception(mapped_paths)
# tmp_paths = dl_manager.extract_and_download(
# dict(
# (
# placeholder_key,
# dict(
# (target, _URLS[target][placeholder_key])
# for target in targets
# ),
# )
# for placeholder_key in ("placeholder")
# ))
# with open(tmp_paths[0], "wb") as out:
# for tmp_path in tmp_paths[1:]:
# with open(tmp_path, "rb") as tmp:
# copyfileobj(tmp, out)
# tmp_path.unlink()
# mapped_paths = mapped_paths.append(tmp_paths[0])
# print(mapped_paths)
return [
datasets.SplitGenerator(
name="train",
gen_kwargs={
"paths": mapped_paths["placeholder"],
"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
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",
)
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)
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"]
yield key, info
key += 1 |