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d658879 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
Licenses, Tasks)
_CITATION = """\
@article{hendria2023msvd,
author = {Willy Fitra Hendria},
title = {MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian},
journal = {arXiv preprint arXiv:2306.11341},
year = {2023},
url = {https://arxiv.org/abs/2306.11341},
}
"""
_DATASETNAME = "id_msvd"
_DESCRIPTION = """\
MSVD-Indonesian is derived from the MSVD (Microsoft Video Description) dataset, which is
obtained with the help of a machine translation service (Google Translate API). This
dataset can be used for multimodal video-text tasks, including text-to-video retrieval,
video-to-text retrieval, and video captioning. Same as the original English dataset, the
MSVD-Indonesian dataset contains about 80k video-text pairs.
"""
_HOMEPAGE = "https://github.com/willyfh/msvd-indonesian"
_LANGUAGES = ["ind"]
_LICENSE = Licenses.MIT.value
_URLS = {"text": "https://raw.githubusercontent.com/willyfh/msvd-indonesian/main/data/MSVD-indonesian.txt", "video": "https://www.cs.utexas.edu/users/ml/clamp/videoDescription/YouTubeClips.tar"}
_SUPPORTED_TASKS = [Tasks.VIDEO_TO_TEXT_RETRIEVAL]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
_LOCAL = False
class IdMsvdDataset(datasets.GeneratorBasedBuilder):
"""MSVD dataset with Indonesian translation."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SEACROWD_SCHEMA_NAME = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower() # "vidtext"
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"video_path": datasets.Value("string"),
"text": datasets.Value("string"),
}
)
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
features = SCHEMA_TO_FEATURES[self.SEACROWD_SCHEMA_NAME.upper()] # video_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
# expect several minutes to download video data ~1.7GB
data_path = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"text_path": Path(data_path["text"]),
"video_path": Path(data_path["video"]) / "YouTubeClips",
"split": "train",
},
),
]
def _generate_examples(self, text_path: Path, video_path: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
text_data = []
with open(text_path, "r", encoding="utf-8") as f:
for line in f:
id = line.find(" ")
video = line[:id]
text = line[id + 1 :].strip()
text_data.append([video, text])
df = pd.DataFrame(text_data, columns=["video_path", "text"])
df["video_path"] = df["video_path"].apply(lambda x: video_path / f"{x}.avi")
if self.config.schema == "source":
for i, row in df.iterrows():
yield i, {
"video_path": str(row["video_path"]),
"text": row["text"],
}
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
for i, row in df.iterrows():
yield i, {
"id": str(i),
"video_path": str(row["video_path"]),
"text": row["text"],
"metadata": {
"resolution": {
"width": None,
"height": None,
},
"duration": None,
"fps": None,
},
}
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