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README.md

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+# LiteAvatar
+We introduce a audio2face model for realtime 2D chat avatar, which can run in 30fps on only CPU devices without GPU acceleration.
+## Pipeline
+- An efficient ASR model from [modelsope](https://modelscope.cn/models/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch) for audio feature extraction.
+- A mouth parameter prediction model given audio feature inputs for voice synchronized mouth movement generation.
+- A lightweight 2D face generator model for mouth movement rendering, which can also be deployed on mobile devices realizing realtime inference.
+## Data Preparation
+Get sample avatar data located in `./data/sample_data.zip` and extract to you path
+## Installation
+We recommend a python version = 3.10 and cuda version = 11.8. Then build environment as follows:
+```shell
+pip install -r requirements.txt
+```
+## Inference
+```
+python lite_avatar.py --data_dir /path/to/sample_data --audio_file /path/to/audio.wav --result_dir /path/to/result
+```
+The mp4 video result will be saved in the result_dir.
+## Interactive demo
+The realtime interactive video chat demo powered by our LiteAvatar algorithm is available at [open-video-chat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat)
+## Acknowledgement
+We are grateful for the following open-source projects that we used in this project:
+- [Paraformer](https://modelscope.cn/models/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch)
+ and [FunASR](https://github.com/modelscope/FunASR) for audio feature extraction.
+## Citation
+If you find this project useful, please ⭐️ star the repository and cite our related paper:
+```
+@inproceedings{ZhuangQZZT22,
+  author       = {Wenlin Zhuang and Jinwei Qi and Peng Zhang and Bang Zhang and Ping Tan},
+  title        = {Text/Speech-Driven Full-Body Animation},
+  booktitle    = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI}},
+  pages        = {5956--5959},
+  year         = {2022}
+}
+```