Delete vox_celeb.py

vox_celeb.py

@@ -1,343 +0,0 @@
-# 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 pandas as pd
-import requests
-
-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": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_parta",
-        "dev": (
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partaa",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partab",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partac",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partad",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partae",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partaf",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partag",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partah",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_mp4_partai",
-        ),
-        "test": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_test_mp4.zip",
-    },
-    "audio1": {
-        "placeholder": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_parta",
-        "dev": (
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partaa",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partab",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partac",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_dev_wav_partad",
-        ),
-        "test": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox1/vox1_test_wav.zip",
-    },
-    "audio2": {
-        "placeholder": "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_parta",
-        "dev": (
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partaa",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partab",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partac",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partad",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partae",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partaf",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partag",
-            "https://huggingface.co/datasets/ProgramComputer/voxceleb/resolve/main/vox2/vox2_dev_aac_partah",
-        ),
-        "test": "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,
-    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 VoxCeleb(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):
-        if dl_manager.is_streaming:
-            raise TypeError("Streaming is not supported for VoxCeleb")
-        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
-            )
-        )
-
-        mapped_paths = dl_manager.extract(
-            dl_manager.download_custom(
-                dict(
-                    (
-                        placeholder_key,
-                        dict(
-                            (target, _URLS[target][placeholder_key])
-                            for target in targets
-                        ),
-                    )
-                    for placeholder_key in ("placeholder", "test")
-                ),
-                download_custom,
-            )
-        )
-
-        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