Datasets:
File size: 2,395 Bytes
eb65429 | 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 | # 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
```bash
# 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.
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