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license: cc-by-nc-4.0
task_categories:
- audio-classification
- video-text-to-text
- question-answering
language:
- en
size_categories:
- 1K<n<10K
tags:
- benchmark
- evaluation
- audio-visual
- synchronization
- vggsound
- multimodal
- mllm
pretty_name: "VGGSync-3K: out-of-domain audio-visual sync benchmark"
configs:
- config_name: default
data_files:
- split: test
path: eval/test_3k.jsonl
---
# VGGSync-3K · out-of-domain audio-visual sync benchmark
Out-of-domain evaluation set used in the paper
**[When Vision Speaks for Sound](https://arxiv.org/abs/2605.16403)**.
Derived from [VGGSoundSync](https://www.robots.ox.ac.uk/~vgg/research/avs/),
this 3,000-clip slice tests whether a video-capable MLLM can detect
audio temporal offsets on **everyday sound events** outside the THUD
in-domain training distribution.
Each item is one VGGSound clip in one of three conditions:
| Condition | Count | `gt_synced` | `gt_direction` | `gt_offset_sec` |
|---|---:|---|---|---|
| Audio aligned (no shift) | 1,000 | `true` | `none` | `0.0` |
| Audio shifted **early** | 1,000 | `false` | `early` | shift amount |
| Audio shifted **delay** | 1,000 | `false` | `delay` | shift amount |
Shift magnitudes span 4 difficulty levels (`very_easy`, `easy`, `medium`,
`hard`); synced clips are tagged `difficulty=synced`.
## What's in this repo
| File | Description |
|---|---|
| `eval/test_3k.jsonl` | Test records — uid / label / paths / ground truth |
| `media.zip` | All referenced videos + audios |
## How to use
```bash
# Download
hf download Rakancorle1/vggsync-3k --repo-type=dataset --local-dir vggsync-3k
cd vggsync-3k
# Unzip media — paths in JSONL resolve automatically
unzip -q media.zip
```
```python
from datasets import load_dataset
ds = load_dataset("Rakancorle1/vggsync-3k")
```
## Record schema
```jsonc
{
"uid": "-r3nM90RCNs_medium_early_0.5s",
"ytid": "-r3nM90RCNs",
"label": "sharpen knife",
"difficulty": "very_easy" | "easy" | "medium" | "hard" | "synced",
"video_path": "videos/<uid>.mp4",
"audio_path": "audios/<uid>.wav",
"gt_synced": true | false,
"gt_direction": "none" | "early" | "delay",
"gt_offset_sec": 0.0 // > 0 for shifted; sign implied by direction
}
```
`uid` follows the pattern `<ytid>_<difficulty>_<direction>_<offset>s`
for shifted clips, and `<ytid>` alone for synced clips.
## Reference eval scripts
Paper evaluation code lives in the
[wvs-code repository](https://github.com/rakanWen/wvs-code) on GitHub.
The JSONL is self-contained — `video_path`, `audio_path`, and the `gt_*`
fields are all you need to compare any model's output against ground truth.
## Citation
```bibtex
@article{wen2026whenvisionspeaksforsound,
title = {When Vision Speaks for Sound},
author = {Xiaofei Wen and Wenjie Jacky Mo and Xingyu Fu and Rui Cai and
Tinghui Zhu and Wendi Li and Yanan Xie and Muhao Chen and Peng Qi},
year = {2026},
url = {https://arxiv.org/abs/2605.16403}
}
```
Please also cite the original VGGSoundSync work:
```bibtex
@InProceedings{Chen21b,
title = "Audio-Visual Synchronization in the Wild",
author = "Honglie Chen, Weidi Xie, Triantafyllos Afouras, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman",
booktitle = "BMVC",
year = "2021"}
```
## Related releases
- **[Rakancorle1/thud-eval](https://huggingface.co/datasets/Rakancorle1/thud-eval)** — in-domain audio-visual Clever Hans benchmark (sync / mute / swap)
- **[Rakancorle1/hans-10k](https://huggingface.co/datasets/Rakancorle1/hans-10k)** — DPO training data
- **[Rakancorle1/hans-sft-4k](https://huggingface.co/datasets/Rakancorle1/hans-sft-4k)** — SFT training data
- **[Collection](https://huggingface.co/collections/Rakancorle1/when-vision-speaks-for-sound)** — everything in one place
## License
This subset is released under **CC-BY-NC-4.0** for the annotations and
shifted-clip pairings. The underlying audio/video content is derived
from VGGSound — please follow VGGSound's licensing terms when using or
redistributing.
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