| ---
|
| 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.
|
|
|
