Datasets:
File size: 4,283 Bytes
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import json
import shutil
import zipfile
from pathlib import Path
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
RAW_FILES = {
"train": "FunQA_train.json",
"validation": "FunQA_val.json",
"test": "FunQA_test.json",
"mcqa_test": "Funqa_mcqa_v1.json",
}
VIDEO_ARCHIVES = {
"validation": "val.zip",
"test": "test.zip",
"train": "train.zip",
}
def load_raw_rows(raw_dir: Path, split: str):
with (raw_dir / RAW_FILES[split]).open("r", encoding="utf-8") as f:
rows = json.load(f)
if split == "validation":
missing_videos = {"C_KT_6_6347_6427.mp4"}
rows = [row for row in rows if row.get("visual_input") not in missing_videos]
return rows
def write_parquet(rows, output_path: Path):
df = pd.DataFrame(rows)
output_path.parent.mkdir(parents=True, exist_ok=True)
if "output" in df.columns:
schema = pa.schema(
[
("instruction", pa.string()),
("visual_input", pa.string()),
("output", pa.string()),
("task", pa.string()),
]
)
df = df[["instruction", "visual_input", "output", "task"]]
else:
schema = pa.schema(
[
("instruction", pa.string()),
("visual_input", pa.string()),
("gt", pa.string()),
("id", pa.string()),
]
)
df = df[["instruction", "visual_input", "gt", "id"]]
table = pa.Table.from_pandas(df, schema=schema, preserve_index=False)
pq.write_table(table, output_path)
def move_raw_files(repo_root: Path, raw_dir: Path):
raw_dir.mkdir(parents=True, exist_ok=True)
for filename in list(RAW_FILES.values()) + list(VIDEO_ARCHIVES.values()):
src = repo_root / filename
dst = raw_dir / filename
if src.exists() and not dst.exists():
shutil.move(str(src), str(dst))
def extract_split_videos(raw_dir: Path, videos_dir: Path, split: str):
archive_name = VIDEO_ARCHIVES[split]
archive_path = raw_dir / archive_name
if not archive_path.exists():
return
videos_dir.mkdir(parents=True, exist_ok=True)
marker = videos_dir / f".{split}_extracted"
if marker.exists():
return
with zipfile.ZipFile(archive_path) as zf:
zf.extractall(videos_dir)
marker.write_text("ok\n", encoding="utf-8")
def normalize_video_layout(videos_dir: Path, split: str):
alias = {"validation": "val"}.get(split, split)
alias_root = videos_dir / alias
expected_root = videos_dir / split
if alias_root.exists() and alias_root != expected_root and not expected_root.exists():
shutil.move(str(alias_root), str(expected_root))
legacy_root = videos_dir / split / split
if legacy_root.exists():
expected_root.mkdir(parents=True, exist_ok=True)
for child in legacy_root.iterdir():
target = expected_root / child.name
if not target.exists():
shutil.move(str(child), str(target))
legacy_alias_root = videos_dir / split / alias
if legacy_alias_root.exists():
expected_root.mkdir(parents=True, exist_ok=True)
for child in legacy_alias_root.iterdir():
target = expected_root / child.name
if not target.exists():
shutil.move(str(child), str(target))
def ensure_layout(repo_root: Path):
move_raw_files(repo_root, repo_root / "raw")
for split in ["test", "validation", "train"]:
extract_split_videos(repo_root / "raw", repo_root / "videos", split)
normalize_video_layout(repo_root / "videos", split)
def main():
parser = argparse.ArgumentParser(description="Build a HF Hub-style FunQA dataset repo with original columns.")
parser.add_argument("--repo-root", type=Path, default=Path("."))
args = parser.parse_args()
repo_root = args.repo_root.resolve()
ensure_layout(repo_root)
raw_dir = repo_root / "raw"
data_dir = repo_root / "data"
for split in RAW_FILES:
rows = load_raw_rows(raw_dir, split)
write_parquet(rows, data_dir / f"{split}.parquet")
print(f"Built parquet splits under: {data_dir}")
if __name__ == "__main__":
main()
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