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license: cc-by-nc-4.0
task_categories:
- audio-to-audio
- automatic-speech-recognition
- video-classification
language:
- zh
- en
- multilingual
tags:
- audiovisual
- speech
- lip-sync
- youtube
- annotations
pretty_name: VoxDub
size_categories:
- 100K<n<1M
---
# VoxDub
**VoxDub** provides segment-level audiovisual annotations derived from public YouTube videos.
This repository hosts the annotation archive (`av_segments_v1.tar.zst`). Raw media is **not** redistributed; reconstruct clips from YouTube using the companion pipeline.
- Dataset: [zyk21/VoxDub](https://huggingface.co/datasets/zyk21/VoxDub)
- Pipeline: see the `av_pipeline` tooling shipped with this release (download → cut → standardize → optional vocal separation)
## Dataset summary
| Item | Value |
|------|--------|
| Videos (YouTube IDs) | ~17,433 |
| Segments | ~766,708 |
| Package | `av_segments_v1.tar.zst` |
| Layout | `datas/{video_id}/{seg_id}.json` |
| License | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
## Files
```
av_segments_v1.tar.zst
└── datas/
└── {youtube_video_id}/
├── S00001.json
├── S00002.json
└── ...
```
Unpack:
```bash
# Python
import zstandard as zstd, tarfile
dctx = zstd.ZstdDecompressor()
with open("av_segments_v1.tar.zst", "rb") as f, dctx.stream_reader(f) as r:
with tarfile.open(fileobj=r, mode="r|") as tar:
tar.extractall("av_segments_v1")
```
Or with the pipeline:
```bash
python pipeline.py extract
```
## Annotation schema
Each JSON file describes one temporal segment of a YouTube video.
| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Segment id (e.g. `S00023`) |
| `start` / `end` | float | Time range in seconds |
| `text_whisper` | string | Whisper transcript |
| `text_paraformer` | string | Paraformer transcript |
| `language` | string | Detected language |
| `language_whisper_prob` | float | Language confidence |
| `wer` | float | Word error rate (ASR comparison) |
| `dnsmos` | float | DNSMOS speech quality score |
| `gender` | int | Speaker gender label |
| `multi_speaker` | float | Multi-speaker score |
| `av_offset` | int | Audio–visual offset (frames) |
| `sync_conf` | float | AV sync confidence |
| `origin_width` / `origin_height` | int | Source resolution |
| `scene_num` | int | Scene index |
| `faces` | object | Per-frame face tracks (`n_frames`, `score`, `bbox`, `landmarks`) |
`faces.n_frames` is typically ≈ `(end - start) * 25`.
### Example
```json
{
"id": "S00023",
"start": 311.432,
"end": 335.618,
"language": "chinese",
"origin_width": 1920,
"origin_height": 1080,
"faces": { "n_frames": 606, "score": [], "bbox": [], "landmarks": [] }
}
```
## Reconstructing media
Annotations alone are not playable media. To obtain aligned clips:
1. Download the YouTube video whose id equals the directory name.
2. Cut `[start, end)`.
3. Standardize to **25 fps**, annotation resolution, **24 kHz** mono audio.
4. (Optional) Run vocal separation for cleaner speech tracks.
Recommended tooling is provided in `av_pipeline` (`pipeline.py`). You will need **yt-dlp**, **ffmpeg**, and **Deno** (YouTube JS runtime) for reliable downloads.
```bash
python pipeline.py run --list-file ids.txt --limit 10
```
## Intended uses
- Audiovisual speech / lip-sync research
- ASR and speech quality benchmarking on in-the-wild video
- Training or evaluating dubbing / talking-head models (non-commercial under CC BY-NC)
## Out-of-scope / limitations
- Does **not** include video or audio binaries
- YouTube videos may be deleted, geo-blocked, or privatized over time
- Transcripts and scores are automatic estimates and may contain errors
- Face landmarks are provided as metadata; respect privacy and platform policies
## Ethical considerations
Use only for research and non-commercial purposes consistent with CC BY-NC 4.0 and YouTube Terms of Service.
Do not attempt to re-identify private individuals beyond what is already public on YouTube.
## Citation
If you use this dataset, please cite the Hub repository:
```bibtex
@misc{voxdub2026,
title = {VoxDub: Audiovisual Speech Segment Annotations},
author = {zyk21},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/zyk21/VoxDub}},
}
```
## License
Annotations are released under **CC BY-NC 4.0**.
Source media remains owned by the original uploaders and subject to YouTube ToS.
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