Ultra-lightweight digital human model that supports real-time operation on mobile devices
Ultralght-Digital-Human is an innovative open source project that makes real-time applications of digital humans on mobile devices possible. It aims to achieve ultra-lightweight digital human models that can run in real time on mobile devices.
Ultra-lightweight digital human model that supports real-time operation on mobile devices
Detailed training and reasoning steps are provided so that users can easily train their own digital humans
Support the use of two different audio feature extraction methods, wenet and hubert, to meet the needs of different scenes
You can use syncnet for better results during training
Application scenarios:
Users can generate digital human identities in real time on mobile devices for use in scenarios such as social applications, games and virtual reality.
Technical details:
The model optimizes the algorithm to run smoothly on low-power devices. Use deep learning technology, combined with image and audio input, to synthesize digital human images in real time.
During the training and deployment process, the model is compressed and pruned to remove redundant parameters to reduce the size and computing requirements of the model. This helps run smoother on mobile devices.
Supports multiple audio feature extraction methods, such as Wenet and Hubert, which can quickly extract key features from audio. This efficient feature extraction helps reduce processing time and resource consumption.
Through optimized data flow and reasoning processes, the model is able to process input data (such as video and audio) in real time, achieving instant digital human responses.
Innovative:
Unlike traditional digital human models that require high-performance hardware, Ultralght-Digital-Human can achieve complex digital human effects on ordinary smartphones, greatly expanding the popularity of its application.
Supports multiple operating systems and platforms and can run on different types of smartphones, increasing its universal applicability.
precautions
Data quality: Ensure that the video and audio used for training are of good quality. The face in the video should be clearly visible, and the audio should be free of noise and interference.
Data preparation: Prepare 3-5 minutes of video containing clear faces, ensuring that the video frame rate meets the requirements (20fps for Wenet, 25fps for Hubert).
Audio feature extraction: Before training, ensure that audio features have been successfully extracted. Wrong feature extraction will affect the effectiveness of model training.
Training parameter adjustment: During the training process, pay attention to adjusting parameters such as learning rate and batch size. Initial settings may require fine-tuning based on training results.
Monitor training progress: Regularly check training logs to monitor loss values and accuracy. If losses do not decrease, parameters may need to be adjusted or data checked.
Use pre-trained models: Try to use pre-trained models as a starting point to speed up training and improve results.
GitHub：https://github.com/anliyuan/Ultralight-Digital-Human
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