test.py

# 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 io import BytesIO 
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
import zipfile

import traceback
import fsspec as fs
import librosa

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": {
        "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["dev"], 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.startswith("audio"):
            features["speaker_name"] = datasets.Value("string")
            features["speaker_nationality"] = datasets.Value("string")
            features["audio"] = datasets.Audio(mono=False)
        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:
        #     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.download_and_extract(
                dict(
                    (
                        placeholder_key,
                        dict(
                            (target, _URLS[target][placeholder_key])
                            for target in targets
                        ),
                    )
                    for placeholder_key in ("dev", "test")
                ))
        
     
        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["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
    
        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",
            )
            for path in paths[conf].values():
                for file in path:
                    # subdirs = [x[0] for x in os.walk(path)]
                    # raise Exception(subdirs)
                    #raise Exception(file)
                    #raise Exception(os.is_symlink(path))
                    #raise Exception(os.listdir(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"):
                        if dataset_format == "aac":
                            with fs.open(info["file"], 'rb') as f:
                                z = BytesIO(f.read())
                                Path('./temp.m4a').write_bytes(z.getbuffer())
                                y, sr = librosa.load('./temp.m4a')
                                #y, sr = librosa.load(f)
                                info["audio"] = {'array':y,'path':info["file"],'sampling_rate':sr}
                        else:
                            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