Replace the audio in a talking-head video and have the lips automatically sync to the new words
Dub a video into another language with AI-generated lip movements that match the translated audio
Experiment with the full training pipeline to fine-tune a lip-sync model on a custom dataset
Run batch inference from the command line to process multiple videos with the same replacement audio
| bytedance/latentsync | agiresearch/aios | google/gemma_pytorch | |
|---|---|---|---|
| Stars | 5,675 | 5,676 | 5,674 |
| Language | Python | Python | Python |
| Setup difficulty | hard | hard | hard |
| Complexity | 4/5 | 5/5 | 4/5 |
| Audience | researcher | researcher | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires a GPU with at least 8 GB of VRAM and CUDA drivers, the 512px model needs 18 GB of VRAM.
LatentSync is a research project from ByteDance that automatically makes a person's lip movements in a video match a different audio track. You give it a video of someone talking and a new audio clip, and it rewrites the mouth area so the lips appear to be saying the new words. This is sometimes called lip-sync or dubbing automation. The system works by combining two existing AI components. It uses Whisper, an audio recognition model, to convert the sound into a format that carries timing and phonetic information. That information is then fed into a modified version of Stable Diffusion, a popular image generation model, which regenerates the face frame by frame so the mouth matches the audio. The whole process happens in one stage rather than two separate steps, which the authors say reduces certain visual artifacts. To run it, you need a computer with a dedicated graphics card. The lighter version (1.5) requires at least 8 GB of video memory, while the higher-resolution version (1.6, which produces 512x512 pixel output) requires 18 GB. You can run inference either through a simple browser-based interface built with Gradio or from the command line. A setup script downloads the required model checkpoints automatically. The repository also includes the full training pipeline for researchers who want to train their own version. This covers data preparation steps such as video segmentation, face alignment, audio resampling, and quality filtering. Training the model from scratch requires substantially more GPU memory, ranging from 23 GB to 55 GB depending on the configuration. LatentSync is released as an open-source research tool. It is intended for research and creative experimentation rather than production deployment.
LatentSync is a ByteDance research tool that automatically replaces lip movements in a video to match a new audio track using Stable Diffusion and Whisper, useful for AI dubbing experiments and creative video editing.
Mainly Python. The stack also includes Python, Stable Diffusion, Whisper.
Open-source research tool from ByteDance, specific license terms are in the repository.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
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