---
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.