Add VoxDub dataset card

README.md

@@ -1,3 +1,155 @@
----
-license: cc-by-nc-4.0
----
+---
+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.