---
pretty_name: WebTalk-Synthetic
license: cc-by-nc-4.0
language:
- en
task_categories:
- audio-to-audio
- other
tags:
- co-speech
- facial-animation
- talking-face
- FLAME
- synthetic
- 3d-motion
size_categories:
- 10K<n<100K
---

# WebTalk-Synthetic

**WebTalk-Synthetic** is a dataset of **synthetic in-the-wild co-speech facial
motion**: ~12.7 k short clips, each pairing conversational speech with a
*generated* 3D facial-motion track in [FLAME](https://flame.is.tue.mpg.de/)
coefficient space. The facial motion is produced by an audio-driven face model
from filtered in-the-wild talking audio — it is synthesized, not
motion-captured. The dataset was created for and used by
[ViBES](https://github.com/Juzezhang/ViBES) (CVPR 2026) to give the face expert
broad in-the-wild coverage.

> ⚠️ **Research use only.** The audio is segmented from public talking-head
> videos. This dataset is released under **CC-BY-NC-4.0 for non-commercial
> research only**. Do not use it for commercial purposes.

## Download

```bash
huggingface-cli download JuzeZhang/WebTalk-Synthetic \
    --repo-type dataset --local-dir WebTalk-Synthetic
```

Each modality is shipped as a single `.tar` (far faster to upload/sync than ~50k
loose files). Extract them in place after download:

```bash
cd WebTalk-Synthetic
for f in audios audios_token_glm FLAME_coeffs_25 transcripts; do tar -xf "$f.tar"; done
# optionally: rm *.tar
```

## Dataset structure

In the repo (as shipped):

```
WebTalk-Synthetic/
├── audios.tar            → audios/            (16 kHz mono WAV, one per clip)
├── audios_token_glm.tar  → audios_token_glm/  (GLM-4-Voice audio tokens, one .npy per clip)
├── FLAME_coeffs_25.tar    → FLAME_coeffs_25/   (synthetic FLAME face motion, one .npz, 25 fps)
├── transcripts.tar        → transcripts/       (one .txt per clip)
├── train.txt              (12,075 clip stems)
├── val.txt                (290 clip stems)
├── test.txt               (304 clip stems)
├── README.md
└── LICENSE
```

`audios_token_glm/<stem>.npy` is a `(N,) int64` array of
[GLM-4-Voice](https://github.com/THUDM/GLM-4-Voice) discrete audio tokens,
provided so you can skip re-running audio tokenization.

Every modality is keyed by a clip stem of the form `<session>_<segment>`
(e.g. `202008647_0001`); `audios/<stem>.wav`, `FLAME_coeffs_25/<stem>.npz`, and
`transcripts/<stem>.txt` all refer to the same clip.

- **12,669 clips total** (12,075 train / 290 val / 304 test).

### FLAME motion format (`FLAME_coeffs_25/<stem>.npz`)

| Key | Shape | dtype | Description |
|---|---|---|---|
| `exp` | `(T, 100)` | float32 | FLAME expression coefficients |
| `shape` | `(T, 100)` | float64 | FLAME shape coefficients |
| `pose` | `(T, 6)` | float32 | head pose (3) + jaw pose (3), axis-angle |
| `mocap_frame_rate` | scalar | int64 | 25 |

`T` is the per-clip frame count at 25 fps.

### Audio

16 kHz mono PCM WAV, ~8 s per clip.

## Usage

Load a clip directly:

```python
import numpy as np, soundfile as sf

stem = "202008647_0001"
audio, sr = sf.read(f"audios/{stem}.wav")          # 16 kHz mono
coef = np.load(f"FLAME_coeffs_25/{stem}.npz")      # exp / shape / pose
text = open(f"transcripts/{stem}.txt").read()
```

The full ViBES preprocessing recipe (audio tokenization, face VQ-VAE
tokenization, and building the training-ready HuggingFace dataset) is documented
in [`docs/1-data/webtalk_synthetic.md`](https://github.com/Juzezhang/ViBES/blob/main/docs/1-data/webtalk_synthetic.md).

## Intended use & limitations

- Intended for **non-commercial research** on co-speech facial animation,
  audio-driven face generation, and conversational virtual humans.
- The facial motion is **model-generated**, not ground-truth capture; it reflects
  the biases and failure modes of the audio-driven face model that produced it.
- Audio originates from public talking-head videos; treat it accordingly and do
  not attempt to re-identify speakers.

## License

[CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/) — non-commercial
research use only.

## Citation

```bibtex
@inproceedings{zhang2026vibes,
  title={ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body},
  author={Juze Zhang and Changan Chen and Xin Chen and Heng Yu and Tiange Xiang and Ali Sartaz Khan and Shrinidhi Kowshika Lakshmikanth and Ehsan Adeli},
  booktitle={CVPR},
  year={2026},
}
```