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README.md

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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/avsync/),
+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{chen2021vggsoundsync,
+  title     = {Audio-Visual Synchronisation in the Wild},
+  author    = {Chen, Honglie and Xie, Weidi and Afouras, Triantafyllos and
+               Nagrani, Arsha and Vedaldi, Andrea and Zisserman, Andrew},
+  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.

media.zip

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+size 5720098739