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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 = [
f"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 = "default"
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"),
"caption": datasets.Value("string"),
"timestamp_start": datasets.Value("float32"),
"timestamp_stop": datasets.Value("float32"),
"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
# Divide video by segments of 15 sec
max_sec_per_segment = 15
last_caption_timestamp = infos["subtitles"][-1]["time"]
num_chunks = (
int(divmod(last_caption_timestamp, max_sec_per_segment)[0]) + 1
)
time_chunks = [
i * max_sec_per_segment for i in range(num_chunks + 1)
]
time_chunk_idx = 0
caption = ""
for el in infos["subtitles"]:
if (
el["time"] > time_chunks[time_chunk_idx + 1]
or el["time"] == last_caption_timestamp
):
timestamp_start = float(time_chunks[time_chunk_idx])
timestamp_stop = float(time_chunks[time_chunk_idx + 1])
time_chunk_idx += 1
metadata_dict = {
"asr_info": infos["denoised"],
"info": infos["info"],
"subtitles": infos["subtitles"],
"title": infos["info"]["title"],
}
yield idx, {
"video_id": id,
"video_url": url,
"caption": caption,
"timestamp_start": timestamp_start,
"timestamp_stop": timestamp_stop
if el["time"] != last_caption_timestamp
else last_caption_timestamp,
"meta": json.dumps(
metadata_dict, default=json_serializer, indent=2
),
}
idx += 1
caption = ""
else:
caption += el["word"] + " "
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