

Ultralight Digital Human
Overview :
Ultralight-Digital-Human is an ultra-lightweight digital human model capable of real-time operation on mobile devices. This model is open-source and, to the developer's knowledge, is the first of its kind to be so lightweight. Its key advantages include a lightweight design suitable for mobile deployment and the capability for real-time performance. It leverages deep learning technologies, especially in the applications of face synthesis and voice simulation, allowing the digital human model to deliver high-quality results with low resource consumption. The product is currently free, primarily targeting tech enthusiasts and developers.
Target Users :
The target audience primarily consists of tech enthusiasts and developers interested in artificial intelligence, deep learning, and digital human technology. They can utilize this model for research, development, or personal projects. Due to its lightweight characteristics, it is also suitable for developers who wish to implement digital human functionalities on resource-constrained devices.
Use Cases
- Utilize the model to create virtual news anchors for broadcasting news.
- In the education sector, create virtual teachers for online teaching.
- In the entertainment industry, produce virtual idols for performance.
Features
- Real-time operation on mobile devices: The model is designed to run smoothly on mobile platforms.
- Open-source code: All code is open-source, enabling community contributions and improvements.
- Easy to train: Detailed training steps are provided, allowing users to effortlessly train their own digital human models.
- Support for various audio feature extractors: Includes wenet and hubert, allowing users to choose based on their needs.
- Streamlined inference support: The model supports streaming inference, suitable for real-time application scenarios.
- Code optimization: Continuous optimization of code to enhance model performance and operational efficiency.
- Community support: An active community supports users in contributing to model improvements via issues and pull requests.
How to Use
1. Install the necessary libraries and environment, such as PyTorch and other dependencies.
2. Download the wenet encoder.onnx file and place it in the designated directory.
3. Prepare video and audio data, and perform preprocessing.
4. Train syncnet for improved performance.
5. Use the trained syncnet model to train the digital human model.
6. Extract test audio features and perform inference.
7. Combine audio and video to generate the final digital human video.
8. Enjoy the results brought by your digital human model.
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