What you need
2 projectsCommercial useMaintainedSetupCost
- Commercial use
- Conditions
- Apache-2.0, so commercial use and modification are allowed. The README adds a statement: videos made with it and published on platforms such as Bilibili, WeChat Channels or Douyin must carry the LiveTalking watermark and logo.
- Maintained
- Active
- Last push on 2026-10-09. The repository is not archived.
- Setup
- Moderate
- Tested by the authors on Ubuntu 22.04, Python 3.12 and CUDA 12.8. You install dependencies, download models from a cloud drive, and start it with one command. The official FAQ lists twelve install errors, mostly dependency version conflicts. A Windows bundle and ready-made cloud images also exist.
- Cost
- Needs a GPU
- The software is free; the GPU is the cost. The authors recommend an RTX 3060 or better for the wav2lip model and an RTX 3080 Ti or better for musetalk. How many avatars can speak at once depends on the GPU; idle sessions depend on the CPU.
- What else you need
- A GPU is required. For speech you can use the free EdgeTTS, host GPT-SoVITS or CosyVoice yourself, or call Tencent Cloud (needs an API key). For it to hold a conversation, add an LLM: Qwen and OpenAI-compatible endpoints are supported (needs an API key). For spoken questions you also need a FunASR recognition service, or the browser’s built-in recognition. The server must expose TCP port 8010 and UDP ports.
- What to watch out for
- The open edition ships a voice-input page, but recognition is on you: run a FunASR service, or use the browser’s built-in speech recognition. Wake-word interruption, full-duplex conversation and ready-made cloud speech recognition are in the paid commercial edition; the docs give no price and say to contact the author.
- Commercial use
- Conditions
- The project itself is Apache-2.0. Of the models it uses, LiteAvatar is MIT, LAM and CosyVoice are Apache-2.0, and MuseTalk has MIT code with a README stating that the trained model may be used commercially. Two need care. SenseVoice, used for speech recognition, ships its weights under the FunASR model license, which requires attribution and keeping the model name. Its section 3 (Responsibility and Risk) says the model is “provided for reference and learning purposes only” and does not say whether that limits commercial use. An issue asks exactly that: a maintainer answered that section 3 is a disclaimer and commercial use is permitted, and in September 2026 that answer was marked as not yet the license owner’s final confirmation. The issue was still open on 2026-10-13. The FlashHead model archive includes one component under Open-RAIL-M; with the Pro variant alone, the whole workflow is Apache-2.0.
- Maintained
- Active
- Last push on 2026-07-31. The repository is not archived.
- Setup
- Moderate
- It needs Python 3.11 and an NVIDIA driver that supports CUDA 12.8. With uv it is three steps: install dependencies, download models, start. Docker also works once the NVIDIA Container Toolkit is installed; the app is then served at https://localhost:8282.
- Cost
- Depends
- The software is free. The docs say the 2D avatar is light on hardware and that avatar inference reaches 30 FPS on an i9-13980HX CPU. Moving speech recognition, the LLM and text-to-speech to cloud APIs lowers the hardware needs a lot, in exchange for paying per call. The docs give no figure for GPU memory.
- What else you need
- The default preset uses Alibaba Cloud Bailian for the LLM and text-to-speech, which needs a DASHSCOPE_API_KEY; speech recognition runs locally with SenseVoice. If you would rather stay off the cloud, the docs say local multimodal models are supported too. For remote access, the bundled coturn can be started alongside it.
- What to watch out for
- The release notes in the README say LiteAvatar supports multiple sessions on one machine, while the FAQ says it does not support concurrency; test it to find out. The stated average response time is 2.2 seconds. The FAQ also notes that personal AutoDL accounts cannot open custom ports, so remote access does not work there.
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