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MuseTalk

App : A product you use directly

Real-time lip-sync running locally.

The lightest option for live lip-sync.

Official site GitHub · 6,793 ★

Overall score

6.0 /10

Security: one caveat, see the card

Utility

8.0

Reliability

2.0

Security

One caveat

Ease

Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Synchronizing lips in a video with a different audio track usually requires heavy tools, often cloud-based, that send your data to third-party servers. MuseTalk solves this by offering an audio-driven lip-sync model that runs entirely locally, with no internet connection needed after installation.

In practice, MuseTalk analyzes the input audio using a Whisper model, encodes frames through a VAE, and generates lip movement in a single pass using a modified UNet architecture with cross-attention. The result is a video where the face region (256x256 pixels) is reworked to match the audio, at over 30 frames per second on an NVIDIA Tesla V100 GPU. Version 1.5, released in March 2025, improves sharpness, identity consistency, and sync accuracy through perceptual loss, GAN loss, and sync loss functions.

MuseTalk is aimed at developers, researchers, and technically minded creators who can set up a Python environment with CUDA. It supports multiple languages (Chinese, English, Japanese) and can be integrated into virtual character pipelines, notably with MuseV. A Gradio interface is available to simplify testing without writing code.

However, MuseTalk is not suited for users without command-line experience or for machines without a dedicated GPU: on a 4 GB VRAM GPU (RTX 3050 Ti), generating 8 seconds of video takes around 5 minutes in fp16 mode.

What it offers

·Audio-driven lip-sync in real time at over 30 frames per second on an NVIDIA Tesla V100 GPU.
·Version 1.5 with improved sharpness, identity consistency, and lip-sync accuracy.
·Multi-language support: Chinese, English, and Japanese.
·Built-in Gradio interface to test parameters without writing code.
·Both normal and real-time inference modes available on Linux and Windows.
·Open-source training code available since April 2025, allowing users to train their own model.
·Third-party integration available via ComfyUI.

Our tips

For best results, use input videos at 25 frames per second, the frame rate used during model training; convert your videos with FFmpeg if needed.
During real-time inference, set the 'preparation' parameter to True only when processing a new avatar for the first time, then switch it to False for subsequent generations with the same avatar.
Use the --use_float16 option in the Gradio interface to reduce VRAM usage and speed up generation, at the cost of a slight quality reduction.
Adjust the center point of the proposed face region: this setting significantly affects the quality of the final output according to the sources.
To generate output without saving intermediate images and speed up processing, add the --skip_save_images flag to the real-time inference command.

Watch out

Installation is complex: it requires Python 3.10, CUDA 11.7, PyTorch 2.0.1, several MMLab packages and FFmpeg, as well as manually downloading multiple model weights from different sources.
On a 4 GB VRAM GPU (RTX 3050 Ti), generating 8 seconds of video takes around 5 minutes in fp16 mode, making the tool poorly suited for quick use on modest hardware.

In practice

Price: Free
Commercial use: Commercial use: yes
Available on: linux, windows
Licence: MIT
Runs locally: Yes, 8 GB VRAM recommended
FR interface: No

Security and data

Rating: Security: one caveat, see the card
Your data: Data stays local
Maintenance: Inactive for over six months

Links and repository checked on 25 October 2026 · added on 1 April 2024

How to install MuseTalk

1. Create a conda environment with Python 3.10: conda create -n MuseTalk python==3.10, then activate it. 2. Install PyTorch 2.0.1 with CUDA 11.8 support via pip or conda. 3. Install Python dependencies: pip install -r requirements.txt. 4. Install MMLab packages (mmengine, mmcv 2.0.1, mmdet 3.1.0, mmpose 1.1.0) using the mim command. 5. Install FFmpeg and configure its path (FFMPEG_PATH variable on Linux, PATH variable on Windows). 6. Download model weights using the provided script (download_weights.sh on Linux or download_weights.bat on Windows) and organize them in the models/ folder; all processing then stays local, no data is sent externally.

conda create -n MuseTalk python==3.10 && conda activate MuseTalk && pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118 && pip install -r requirements.txt

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