---
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.