• 2026.01.08 - We have released the inference code, and the model weights.
• 2025.12.30 - We released Project page on SoulX-FlashTalk.
• 2025.12.30 - We released SoulX-FlashTalk Technical Report on Arxiv and GitHub repository.

A 4-GPU version of SoulX-FlashTalk and a new open-source real-time streaming digital human model designed specifically for consumer-grade GPUs like 4090 etc.

• Technical report
• Project Page
• Inference code
• Checkpoint release
• Online demo

conda create -n flashtalk python=3.10
conda activate flashtalk

pip install torch==2.7.1 torchvision==0.22.1 --index-url https://download.pytorch.org/whl/cu128

pip install -r requirements.txt

pip install ninja
pip install flash_attn==2.8.0.post2 --no-build-isolation

# Ubuntu / Debian
apt-get install ffmpeg
# CentOS / RHEL
yum install ffmpeg ffmpeg-devel

or

# Conda (no root required) 
conda install -c conda-forge ffmpeg==7

Model Component	Description	Link
SoulX-FlashTalk-14B	Our 14b model	🤗 Huggingface
chinese-wav2vec2-base	chinese-wav2vec2-base	🤗 Huggingface

# If you are in china mainland, run this first: export HF_ENDPOINT=https://hf-mirror.com
pip install "huggingface_hub[cli]"
huggingface-cli download Soul-AILab/SoulX-FlashTalk-14B --local-dir ./models/SoulX-FlashTalk-14B
huggingface-cli download TencentGameMate/chinese-wav2vec2-base --local-dir ./models/chinese-wav2vec2-base

# Infer on single GPU
# Requires more than 64G of VRAM. Use --cpu_offload to reduce VRAM usage to 40G.
bash inference_script_single_gpu.sh

# Infer on multy GPUs
# Real-time inference speed can only be supported on 8xH800 or higher graphics cards
bash inference_script_multi_gpu.sh

Coming Soon!

If you are interested in leaving a message to our work, feel free to email le.shen@mail.dhu.edu.cn or qiaoqian@soulapp.cn or yutan@soulapp.cn or zhouke@soulapp.cn or liusiyuan@soulapp.cn

Due to Group 1 reaching its capacity, we have opened a new WeChat group. Additionally, we represent SoulApp and warmly welcome everyone to download the app and join our Soul group for further technical discussions and updates!

If you find our work useful in your research, please consider citing:

@misc{shen2025soulxflashtalktechnicalreport,
      title={SoulX-FlashTalk: Real-Time Infinite Streaming of Audio-Driven Avatars via Self-Correcting Bidirectional Distillation}, 
      author={Le Shen and Qian Qiao and Tan Yu and Ke Zhou and Tianhang Yu and Yu Zhan and Zhenjie Wang and Ming Tao and Shunshun Yin and Siyuan Liu},
      year={2025},
      eprint={2512.23379},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.23379}, 
}

• Infinitetalk and Wan: the base model we built upon.
• Self forcing: the codebase we built upon.
• DMD and Self forcing++: the key distillation technique used by our method.