(QQ群: 792615362）

User Survey: https://forms.gle/w6Y6PiHTZr1nzbtWA

调查问卷(中文): https://docs.qq.com/form/page/DTERQRkJMRWNiSldI

⚠️ Read this if you are updating from an old version without the voice interruption feature: The latest version changed how to open the live2d server and the backend: server.py now launches everything it needs (except the browser). To run with Live2D and the browser, launch server.py and open the web page in the browser. You no longer need to run main.py with the server.py. Running server.py assumes Live2D mode with the browser, and running main.py assumes no Live2D mode without the browser. In addition, options MIC-IN-BROWSER and LIVE2D in the configuration file no longer have any effects and have been deprecated due to the changes in the backend.

⚠️ This project is in its early stages and is currently under active development. Features are unstable, code is messy, and breaking changes will occur. The main goal of this stage is to build a minimum viable prototype using technologies that are easy to integrate.

⚠️ This project currently has a lot of issues on Windows. In theory, it should all work, but many people using Windows have many problems with many dependencies. I might fix those features in the future, but Windows support currently requires testing and debugging. If you have a Mac or a Linux machine, use them instead for the time being. Join the Discord server if you need help or to get updates about this project.

⚠️ If you want to run this program on a server and access it remotely on your laptop, the microphone on the front end will only launch in a secure context (a.k.a. https or localhost). See MDN Web Doc. Therefore, you might want to configure https with a reverse proxy if you want to access the page on a remote machine (non-localhost).

Open-LLM-VTuber allows you to talk to (and interrupt!) any LLM locally by voice (hands-free) with a Live2D talking face. The LLM inference backend, speech recognition, and speech synthesizer are all designed to be swappable. This project can be configured to run offline on macOS, Linux, and Windows. Online LLM/ASR/TTS options are also supported.

Long-term memory with MemGPT can be configured to achieve perpetual chat, infinite* context length, and external data source.

This project started as an attempt to recreate the closed-source AI VTuber neuro-sama with open-source alternatives that can run offline on platforms other than Windows.

English demo:

中文 demo:

BiliBili, YouTube

• It works on macOS
  • Many existing solutions display Live2D models with VTube Studio and achieve lip sync by routing desktop internal audio into VTube Studio and controlling the lips with that. On macOS, however, there is no easy way to let VTuber Studio listen to internal audio on the desktop.
  • Many existing solutions lack support for GPU acceleration on macOS, which makes them run slow on Mac.
• This project supports MemGPT for perpetual chat. The chatbot remembers what you've said.
• No data leaves your computer if you wish to
  • You can choose local LLM/voice recognition/speech synthesis solutions; everything works offline. Tested on macOS.
• You can interrupt the LLM anytime with your voice without wearing headphones.

• Chat with any LLM by voice
• Interrupt LLM with voice at any time
• Choose your own LLM backend
• Choose your own Speech Recognition & Text to Speech provider
• Long-term memory
• Live2D frontend

• macOS
• Linux
• Windows

• [Sep 17, 2024] Added DeepLX translation to change the language for audio
• [Sep 6, 2024] Added GroqWhisperASR
• [Sep 5, 2024] Better Docker support
• [Sep 1, 2024] Added voice interruption (and refactored the backend)
• [Jul 15, 2024] Added MeloTTS
• [Jul 15, 2024] Refactored llm and launch.py and reduced TTS latency
• [Jul 11, 2024] Added CosyVoiceTTS
• [Jul 11, 2024] Added FunASR with SenseVoiceSmall speech recognition model.
• [Jul 7, 2024] Totally untested Docker support with Nvidia GPU passthrough (no Mac, no AMD)
• [Jul 6, 2024] Support for Chinese 支持中文 and probably some other languages...
• [Jul 6, 2024] WhisperCPP with macOS GPU acceleration. Dramatically decreased latency on Mac
• ...

• Talk to LLM with voice. Offline.
• RAG on chat history (temporarily removed)

Currently supported LLM backend

• Any OpenAI-API-compatible backend, such as Ollama, Groq, LM Studio, OpenAI, and more.
• MemGPT (setup required)

Currently supported Speech recognition backend

• FunASR, which support SenseVoiceSmall and many other models. (Local Currently requires an internet connection for loading. Compute locally)
• Faster-Whisper (Local)
• Whisper-CPP using the python binding pywhispercpp (Local, mac GPU acceleration can be configured)
• Whisper (local)
• Groq Whisper (API Key required). This is a hosted Whisper endpoint, which is fast and has a generous free limit every day.
• Azure Speech Recognition (API Key required)
• The microphone in the server terminal will be used by default. You can change the setting MIC_IN_BROWSER in the conf.yaml to move the microphone (and voice activation detection) to the browser (at the cost of latency, for now). You might want to use the microphone on your client (the browser) rather than the one on your server if you run the backend on a different machine or inside a VM or docker.

Currently supported Text to Speech backend

• py3-tts (Local, it uses your system's default TTS engine)
• meloTTS (Local, fast)
• Coqui-TTS (Local, speed depends on the model you run.)
• bark (Local, very resource-consuming)
• CosyVoice (Local, very resource-consuming)
• xTTSv2 (Local, very resource-consuming)
• Edge TTS (online, no API key required)
• Azure Text-to-Speech (online, API Key required)

Fast Text Synthesis

• Synthesize sentences as soon as they arrive, so there is no need to wait for the entire LLM response.
• Producer-consumer model with multithreading: Audio will be continuously synthesized in the background. They will be played one by one whenever the new audio is ready. The audio player will not block the audio synthesizer.

Live2D Talking face

• Change Live2D model with config.yaml (model needs to be listed in model_dict.json)
• Load local Live2D models. Check doc/live2d.md for documentation.
• Uses expression keywords in LLM response to control facial expression, so there is no additional model for emotion detection. The expression keywords are automatically loaded into the system prompt and excluded from the speech synthesis output.

live2d technical details

• Uses guansss/pixi-live2d-display to display live2d models in browser
• Uses WebSocket to control facial expressions and talking state between the server and the front end
• All the required packages are locally available, so the front end works offline.
• You can load live2d models from a URL or the one stored locally in the live2d-models directory. The default shizuku-local is stored locally and works offline. If the URL property of the model in the model_dict.json is a URL rather than a path starting with /live2d-models, they will need to be fetched from the specified URL whenever the front end is opened. Read doc/live2d.md for documentation on loading your live2D model from local.
• Run the server.py to run the WebSocket communication server, open the index.html in the ./static folder to open the front end, and run launch.py main.py to run the backend for LLM/ASR/TTS processing.

New installation instruction is being created here

• ffmpeg
• Python >= 3.10, < 3.13
• More testing needs to be done on Python 3.13

Clone this repository.

You need to have Ollama or any other OpenAI-API-Compatible backend ready and running. If you want to use MemGPT as your backend, scroll down to the MemGPT section.

Prepare the LLM of your choice. Edit the BASE_URL and MODEL in the project directory's conf.yaml.

Virtual Python environment like conda or venv is strongly recommended! (because the dependencies are a mess!).

Run the following in the terminal to install the dependencies.

pip install -r requirements.txt # Run this in the project directory 
# Install Speech recognition dependencies and text-to-speech dependencies according to the instructions below

This project, by default, launches the audio interaction mode, meaning you can talk to the LLM by voice, and the LLM will talk back to you by voice.

Edit the conf.yaml for configurations. You can follow the configuration used in the demo video.

If you want to use live2d, run server.py. Open the page localhost:12393 (you can change this) with your browser, and you are ready. Once the live2D model appears on the screen, it's ready to talk to you.

If you don't want the live2d, you can run main.py with Python for cli mode.

Some models will be downloaded on your first launch, which may require an internet connection and may take a while.

Back up the configuration files conf.yaml if you've edited them, and then update the repo. Or just clone the repo again and make sure to transfer your configurations. The configuration file will sometimes change because this project is still in its early stages. Be cautious when updating the program.

Edit the ASR_MODEL settings in the conf.yaml to change the provider.

Here are the options you have for speech recognition:

FunASR (local) (Runs very fast even on CPU. Not sure how they did it)

• FunASR is a Fundamental End-to-End Speech Recognition Toolkit from ModelScope that runs many ASR models. The result and speed are pretty good with the SenseVoiceSmall from FunAudioLLM at Alibaba Group.
• Install with pip install -U funasr modelscope huggingface_hub. Also, ensure you have torch (torch>=1.13) and torchaudio. Install them with pip install torch torchaudio
• It requires an internet connection on launch even if the models are locally available. See modelscope/FunASR#1897

Faster-Whisper (local)

• Whisper, but faster. On macOS, it runs on CPU only, which is not so fast, but it's easy to use.
• For Nvidia GPU users, to use GPU acceleration, you need the following NVIDIA libraries to be installed:
  • cuBLAS for CUDA 12
  • cuDNN 8 for CUDA 12
• Or if you don't need the speed, you can set the device setting under Faster-Whisper in conf.yaml to cpu to reduce headaches.

WhisperCPP (local) (runs super fast on a Mac if configured correctly)

• If you are on a Mac, read below for instructions on setting up WhisperCPP with coreML support. If you want to use CPU or Nvidia GPU, install the package by running pip install pywhispercpp.
• The whisper cpp python binding. It can run on coreML with configuration, which makes it very fast on macOS.
• On CPU or Nvidia GPU, it's probably slower than Faster-Whisper

WhisperCPP coreML configuration:

• Uninstall the original pywhispercpp if you have already installed it. We are building the package.
• Run install_coreml_whisper.py with Python to automatically clone and build the coreML-supported pywhispercpp for you.
• Prepare the appropriate coreML models.
  • You can either convert models to coreml according to the documentation on Whisper.cpp repo
  • ...or you can find some magical huggingface repo that happens to have those converted models. Just remember to decompress them. If the program fails to load the model, it will produce a segmentation fault.
  • You don't need to include those weird prefixes in the model name in the conf.yaml. For example, if the coreML model's name looks like ggml-base-encoder.mlmodelc, just put base into the model_name under WhisperCPP settings in the conf.yaml.

Whisper (local)

• Original Whisper from OpenAI. Install it with pip install -U openai-whisper
• The slowest of all. Added as an experiment to see if it can utilize macOS GPU. It didn't.

GroqWhisperASR (online, API Key required)

• Whisper endpoint from Groq. It's very fast and has a lot of free usage every day. It's pre-installed. Get an API key from groq and add it into the GroqWhisper setting in the conf.yaml.
• API key and internet connection are required.

AzureASR (online, API Key required)

• Azure Speech Recognition. Install with pip install azure-cognitiveservices-speech.
• API key and internet connection are required.
• ⚠️ ‼️ The api_key.py was deprecated in v0.2.5. Please set api keys in conf.yaml.

Install the respective package and turn it on using the TTS_MODEL option in conf.yaml.

pyttsx3TTS (local, fast)

• Install with the command pip install py3-tts.
• This package will use the default TTS engine on your system. It uses sapi5 on Windows, nsss on Mac, and espeak on other platforms.
• py3-tts is used instead of the more famous pyttsx3 because pyttsx3 seems unmaintained, and I couldn't get the latest version of pyttsx3 working.

meloTTS (local, fast)

• Install MeloTTS according to their documentation (don't install via docker) (A nice place to clone the repo is the submodule folder, but you can put it wherever you want). If you encounter a problem related to mecab-python, try this fork (hasn't been merging into the main as of July 16, 2024).
• It's not the best, but it's definitely better than pyttsx3TTS, and it's pretty fast on my mac. I would choose this for now if I can't access the internet (and I would use edgeTTS if I have the internet).

coquiTTS (local, can be fast or slow depending on the model you run)

• Seems easy to install
• Install with the command pip install "coqui-tts[languages]"
• Support many different TTS models. List all supported models with tts --list_models command.
• The default model is an english only model.
• Use tts_models/zh-CN/baker/tacotron2-DDC-GST for Chinese model. (but the consistency is weird...)
• If you found some good model to use, let me know! There are too many models I don't even know where to start...

barkTTS (local, slow)

• Install the pip package with this command pip install git+https://github.com/suno-ai/bark.git and turn it on in conf.yaml.
• The required models will be downloaded on the first launch.

cosyvoiceTTS (local, slow)

• Configure CosyVoice and launch the WebUI demo according to their documentation.
• Edit conf.yaml to match your desired configurations. Check their WebUI and the API documentation on the WebUI to see the meaning of the configurations under the setting cosyvoiceTTS in the conf.yaml.

xTTSv2 (local, slow)

• Recommend to use xtts-api-server, it has clear api docs and relative easy to deploy.

edgeTTS (online, no API key required)

• Install the pip package with this command pip install edge-tts and turn it on in conf.yaml.
• It sounds pretty good. Runs pretty fast.
• Remember to connect to the internet when using edge tts.

AzureTTS (online, API key required) (This is the exact same TTS used by neuro-sama)

• Install the Azure SDK with the commandpip install azure-cognitiveservices-speech.
• Get an API key (for text to speech) from Azure.
• ⚠️ ‼️ The api_key.py was deprecated in v0.2.5. Please set api keys in conf.yaml.
• The default settings in the conf.yaml is the voice used by neuro-sama.

If you're using macOS, you need to enable the microphone permission of your terminal emulator (you run this program inside your terminal, right? Enable the microphone permission for your terminal). If you fail to do so, the speech recognition will not be able to hear you because it does not have permission to use your microphone.

DeepLX translation was implemented to let the program speaks in a language different from the conversation language. For example, the LLM might be thinking in English, the subtitle is in English, and you are speaking English, but the voice of the LLM is in Japanese. This is achieved by translating the sentence before it was sent for audio generation.

DeepLX is the only supported translation backend for now. Other providers will be implemented soon.

1. Set TRANSLATE_AUDIO in conf.yaml to True
2. Set DEEPLX_TARGET_LANG to your desired language. Make sure this language matches the language of the TTS speaker (for example, if the DEEPLX_TARGET_LANG is "JA", which is Japanese, the TTS should also be speaking Japanese.).

⚠️ MemGPT has been renamed to Letta and changed a bunch of things related to its API and how things functions. As of now, the integration of MemGPT in this project is not updated with the latest changes.

MemGPT integration is very experimental and requires quite a lot of setup. In addition, MemGPT requires a powerful LLM (larger than 7b and quantization above Q5) with a lot of token footprint, which means it's a lot slower. MemGPT does have its own LLM endpoint for free, though. You can test things with it. Check their docs.

This project can use MemGPT as its LLM backend. MemGPT enables LLM with long-term memory.

To use MemGPT, you need to have the MemGPT server configured and running. You can install it using pip or docker or run it on a different machine. Check their GitHub repo and official documentation.

⚠️ I recommend you install MemGPT either in a separate Python virtual environment or in docker because there is currently a dependency conflict between this project and MemGPT (on fast API, it seems). You can check this issue Can you please upgrade typer version in your dependancies #1382.

Here is a checklist:

• Install memgpt
• Configure memgpt
• Run memgpt using memgpt server command. Remember to have the server running before launching Open-LLM-VTuber.
• Set up an agent either through its cli or web UI. Add your system prompt with the Live2D Expression Prompt and the expression keywords you want to use (find them in model_dict.json) into MemGPT
• Copy the server admin password and the Agent id into ./llm/memgpt_config.yaml. By the way, agent id is not the agent's name.
• Set the LLM_PROVIDER to memgpt in conf.yaml.
• Remember, if you use memgpt, all LLM-related configurations in conf.yaml will be ignored because memgpt doesn't work that way.

Another long term memory solution. Still in development. Highly experimental.

Pro

• It's easier to set up compare to MemGPT
• It's a bit faster than MemGPT (but still would take quite a lot more LLM tokens to process)

Cons

• It remembers your preference and thoughts, nothing else. It doesn't remember what the LLM said.
• It doesn't always put stuff into memory.
• It sometimes remember wrong stuff
• It requires an LLM with very good function calling capability, which is quite difficult for smaller models

PortAudio Missing

• Install libportaudio2 to your computer via your package manager like apt

⚠️ This is highly experimental, but I think it works. Most of the time.

You can either build the image youself or pull it from the docker hub.

• (but the image size is crazy large)
• The image on the docker hub might not updated as regularly as it can be. GitHub action can't build an image as big as this. I might look into other options.

Current issues:

• Large image size (~20GB), and will require more space because some models are optional and will be downloaded only when used.
• Nvidia GPU required (GPU passthrough limitation)
• Nvidia Container Toolkit needs to be configured for GPU passthrough.
• Some models will have to be downloaded again if you stop the container. (will be fixed)
• Don't build the image on an Arm machine. One of the dependencies (grpc, to be exact) will fail for some reason grpc/grpc#34998.
• And as mentioned before, you can't run it on a remote server unless the web page has https. That's because the web mic on the front end will only launch in a secure context (which means localhost or https environment only).

Most of the asr and tts will be pre-installed. However, bark TTS and the original OpenAI Whisper (Whisper, not WhisperCPP) are NOT included in the default build process because they are huge (~8GB, which makes the whole container about 25GB). In addition, they don't deliver the best performance either. To include bark and/or whisper in the image, add the argument --build-arg INSTALL_ORIGINAL_WHISPER=true --build-arg INSTALL_BARK=true to the image build command.

Setup guide:

1. Review conf.yaml before building (currently burned into the image, I'm sorry):

2. Build the image:

docker build -t open-llm-vtuber .

(Grab a drink, this will take a while)

3. Grab a conf.yaml configuration file. Grab a conf.yaml file from this repo. Or you can get it directly from this link.

4. Run the container:

$(pwd)/conf.yaml should be the path of your conf.yaml file.

docker run -it --net=host --rm -v $(pwd)/conf.yaml:/app/conf.yaml -p 12393:12393 open-llm-vtuber

5. Open localhost:12393 to test

(this project is in the active prototyping stage, so many things will change)

Some abbreviations used in this project:

• LLM: Large Language Model
• TTS: Text-to-speech, Speech Synthesis, Voice Synthesis
• ASR: Automatic Speech Recognition, Speech recognition, Speech to text, STT
• VAD: Voice Activation Detection

You can assume that the sample rate is 16000 throughout this project. The frontend stream chunks of Float32Array with a sample rate of 16000 to the backend.

1. Implement TTSInterface defined in tts/tts_interface.py.
2. Add your new TTS provider into tts_factory: the factory to instantiate and return the tts instance.
3. Add configuration to conf.yaml. The dict with the same name will be passed into the constructor of your TTSEngine as kwargs.

1. Implement ASRInterface defined in asr/asr_interface.py.
2. Add your new ASR provider into asr_factory: the factory to instantiate and return the ASR instance.
3. Add configuration to conf.yaml. The dict with the same name will be passed into the constructor of your class as kwargs.

1. Implement LLMInterface defined in llm/llm_interface.py.
2. Add your new LLM provider into llm_factory: the factory to instantiate and return the LLM instance.
3. Add configuration to conf.yaml. The dict with the same name will be passed into the constructor of your class as kwargs.

Awesome projects I learned from

• https://github.com/dnhkng/GlaDOS
• https://github.com/SchwabischesBauernbrot/unsuperior-ai-waifu
• https://codepen.io/guansss/pen/oNzoNoz
• https://github.com/Ikaros-521/AI-Vtuber
• https://github.com/zixiiu/Digital_Life_Server