scripts/README.md

Video preparation scripts

These scripts help you assemble the source videos that the SFT dataset references. The videos themselves are NOT redistributed in this repo for licensing reasons; only annotations and tool-returned frames are included.

What is referenced

Each parquet row has a video_relpath column such as:

LongVT/videos/0b8d0b9e-...mp4
VideoVista/videos/xxx.mp4
LLaVA-Video-178K/nextqa/videos/xxx.mp4
CinePile/videos/xxx.mp4

You need to make these paths resolve from wherever you use the dataset.

Quick start

# 1. Download the SFT dataset
huggingface-cli download MihailSlutsky/videotir-sft-v2 --local-dir ./videotir-sft-v2
cd ./videotir-sft-v2
tar xzf images.tar.gz

# 2. Download the original source videos
HF_TOKEN=xxx python scripts/download_sources.py \
    --sft-dir ./videotir-sft-v2 \
    --output-dir ./videotir-sft-v2-videos

# 3. Build a single video tree aligned with video_relpath
python scripts/prepare_videos.py \
    --sft-dir ./videotir-sft-v2 \
    --video-source-dir ./videotir-sft-v2-videos \
    --output-dir ./videotir-sft-v2/videos \
    --verify

After step 3, videotir-sft-v2/videos/<video_relpath> should exist for every row.

Source-specific notes

LongVT / VideoVista

• LongVT: EvolvingLMMs-Lab/LongVT
• VideoVista: MasterBin-IIAU/VideoVista
• Accept the dataset license on HuggingFace before downloading.

LLaVA-Video-178K

• Repo: lmms-lab/LLaVA-Video-178K
• Videos are packed in per-split tar.gz archives (e.g. 0_30_s_nextqa/0_30_s_nextqa_videos_1.tar.gz). download_sources.py downloads the archives, extracts the needed files, and flattens them into LLaVA-Video-178K/<split>/videos/.

CinePile

• Repo: joezid/cinepile
• Videos are under the videos/ folder in that dataset.

Ego4D

• Full raw videos are not available from HuggingFace.
• You must accept the Ego4D license at https://ego4d-data.org/ and download the videos yourself.
• download_sources.py will create a placeholder file explaining this; prepare_videos.py will then be able to symlink your local Ego4D clips if you place them at videotir-sft-v2-videos/ego4d_nlq/videos/.

Manual fallback

If a source is not reachable from your current HuggingFace endpoint, you can manually download the videos and just use prepare_videos.py with --video-source-dir pointing at your local tree.