| import os | |
| import json | |
| import datasets | |
| import datetime | |
| _CITATION = """ | |
| @inproceedings{zellersluhessel2021merlot, | |
| title={MERLOT: Multimodal Neural Script Knowledge Models}, | |
| author={Zellers, Rowan and Lu, Ximing and Hessel, Jack and Yu, Youngjae and Park, Jae Sung and Cao, Jize and Farhadi, Ali and Choi, Yejin}, | |
| booktitle={Advances in Neural Information Processing Systems 34}, | |
| year={2021} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| YT-Temporal-180M, a large and diverse dataset of 6 million videos (spanning 180M extracted frames) | |
| that covers diverse topics. | |
| """ | |
| _URL_BASE = "https://rowanzellers.com/merlot/#data" | |
| url_numbers = ["00" + str(i) if i<10 else "0"+ str(i) for i in range(100)] | |
| _DL_URLS = ["https://storage.googleapis.com/merlot/yttemporal180m/yttemporal180m_{num}of100.jsonl.gz" for num in url_numbers] | |
| def json_serializer(o): | |
| if isinstance(o, datetime): | |
| return str(o) | |
| raise TypeError( | |
| f"Object of type {o.__class__.__name__} is not JSON serializable") | |
| class yttemporal180mConfig(datasets.BuilderConfig): | |
| """BuilderConfig for ActivityNet Captions.""" | |
| def __init__(self, **kwargs): | |
| super(yttemporal180mConfig, self).__init__( | |
| version=datasets.Version("2.1.0", ""), **kwargs) | |
| class yttemporal180m(datasets.GeneratorBasedBuilder): | |
| DEFAULT_CONFIG_NAME = "all" | |
| BUILDER_CONFIGS = [ | |
| yttemporal180mConfig( | |
| name="default", description="Default full yttemporal180m dataset"), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "video_id": datasets.Value("string"), | |
| "video_url": datasets.Value("string"), | |
| "asr": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "quality": datasets.Value("int8"), | |
| "meta": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_URL_BASE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| archive_paths = [dl_manager.download_and_extract(url) for url in _DL_URLS] | |
| train_split = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "jsonl_files": archive_paths | |
| }, | |
| ) | |
| ] | |
| return train_split | |
| def _generate_examples(self, jsonl_files): | |
| """This function returns the examples.""" | |
| idx = 0 | |
| for file in jsonl_files: | |
| with open(file, encoding="utf-8") as jsonl_file: | |
| json_list = list(jsonl_file) | |
| for json_str in json_list: | |
| infos = json.loads(json_str) | |
| id = infos['info']['display_id'] | |
| url = "https://www.youtube.com/watch?v=" + id[2:] | |
| asr = "" | |
| for example in infos['denoised']: | |
| asr += example["cleanasr"] | |
| metadata_dict = { | |
| "asr_info": infos["denoised"], | |
| "info": infos["info"], | |
| "subtitles": infos["subtitles"], | |
| } | |
| yield idx, { | |
| "video_id": id, | |
| "video_url": url, | |
| "asr": asr, | |
| "title": infos['info']['title'], | |
| "quality": infos['info']['quality'], | |
| "meta": json.dumps( | |
| metadata_dict, | |
| default=json_serializer, | |
| indent=2 | |
| ) | |
| } | |
| idx += 1 |