Any-to-Any
Transformers
Safetensors
funaudiochat
text-generation
audio-visual
spoken-dialogue
talking-head
streaming
Instructions to use junseok520/JAVIS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junseok520/JAVIS with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("junseok520/JAVIS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from junseok520/JAVIS: direct link, hf CLI and curl.
- Browser
- Download file 2.24 kB
-
https://huggingface.co/junseok520/JAVIS/resolve/main/README.md
- Command line
-
hf download hf://junseok520/JAVIS/README.md
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curl -L -o README.md https://huggingface.co/junseok520/JAVIS/resolve/main/README.md
2.24 kB
| license: cc-by-nc-4.0 | |
| library_name: transformers | |
| pipeline_tag: any-to-any | |
| tags: | |
| - audio-visual | |
| - spoken-dialogue | |
| - talking-head | |
| - streaming | |
| # JAVIS | |
| JAVIS is an end-to-end, streaming spoken-dialogue agent that answers with voice and face at the same time. Built on | |
| Fun-Audio-Chat-8B, it generates the reply text, CosyVoice3 speech tokens and 20-d FLOAT motion coefficients in one | |
| autoregressive pass; CosyVoice3 and the FLOAT decoder render the waveform and a 512x512 talking face packet by packet. | |
| [Paper](https://arxiv.org/abs/2610.22499) · [Code](https://github.com/kaistmm/JAVIS) · [Project page](https://mm.kaist.ac.kr/projects/JAVIS) | |
| ## Files | |
| - `model-*.safetensors`, `config.json`, tokenizer and processor files: the JAVIS model (the Fun-Audio-Chat LM with the | |
| trained Joint Refined Head LoRA merged in and the Motion Decoder) | |
| - `direction_Q.pt`: the FLOAT motion basis (20 x 512) used to render the predicted motion | |
| ## Usage | |
| ```bash | |
| git clone --recursive https://github.com/kaistmm/JAVIS.git && cd JAVIS | |
| huggingface-cli download junseok520/JAVIS --local-dir pretrained_models/JAVIS | |
| python inference/infer.py --audio examples/user.wav --portrait examples/portrait.png --voice_ref examples/voice_ref.wav | |
| ``` | |
| See the code repository for installation, real-time streaming and training. | |
| ## License | |
| The weights are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (non-commercial use, with | |
| attribution; full text in `LICENSE`). The model builds on Fun-Audio-Chat-8B (Apache-2.0), but it predicts motion in the | |
| [FLOAT](https://github.com/deepbrainai-research/float) latent space, renders it with the FLOAT decoder, and was trained | |
| on data that includes non-commercial sources (e.g. Seamless Interaction, CC BY-NC 4.0). FLOAT itself is licensed under | |
| CC BY-NC-ND 4.0 (non-commercial, research use only; see its README). The inference code on GitHub is Apache-2.0. | |
| ## Citation | |
| ```bibtex | |
| @article{ahn2026javis, | |
| title = {JAVIS: Joint Audio-Visual Interaction and Streaming for Real-Time Conversational Agents}, | |
| author = {Ahn, Junseok and Park, Jaehyun and Choi, Jeongsoo and Chung, Joon Son and Kim, Ji-Hoon}, | |
| journal = {arXiv preprint arXiv:2610.22499}, | |
| year = {2026} | |
| } | |
| ``` | |