Skip to content

HumanAIGC-Engineering/OpenAvatarChat

BranchesTags

Repository files navigation

Open Avatar Chat

English | 中文

A modular interactive digital human conversation implementation that runs full-featured on a single PC.

🤗 Demo  |  Static Badge Demo  |  💬 WeChat

🔥 Core Highlights

  • Low-latency digital human real-time conversation: The average response delay is about 2.2 seconds.
  • Multimodal language model: Supports multimodal language models including text, audio, video, etc.
  • Modular design: Uses modular design, allowing flexible component replacement to achieve different function combinations.

📢 News

Changelog

  • [2025.06.12] ⭐️⭐️⭐️ Version 0.4.1 Released:
    • Added support for MuseTalk, including customizable videos for personalized avatars.
    • Released 50 new LiteAvatar styles featuring a variety of professional roles. Please refer to LiteAvatarGallery.
  • [2025.04.18] ⭐️⭐️⭐️ Version 0.3.0 Released:
    • 🎉🎉🎉 Congratulations to the LAM team on their paper being accepted to SIGGRAPH 2025! 🎉🎉🎉
    • Added support for LAM in digital humans, enabling concurrent configuration when LAM is selected. TTS now supports edge_tts and BaiLian CosyVoice.
    • Updated dependency management approach based on UV and handler modules, supporting direct execution or using Docker.
    • CSS responsive layout updated.
  • [2025.04.14] ⭐️⭐️⭐️ Version 0.2.2 released:
  • [2025.04.07] ⭐️⭐️⭐️ Version 0.2.1 released:
    • Added support for history logging
    • Support for text input
    • Camera requirement removed at startup
    • Optimized modular loading method
  • [2025.02.20] ⭐️⭐️⭐️ Version 0.1.0 released:
    • Modular real-time interactive digital human
    • Supports MiniCPM-o as a multimodal language model with cloud API options

Todo List

  • Pre-set digital human models reach 100
  • Integrated LAM: An open-source project capable of creating ultra-realistic 3D digital humans from a single image in seconds
  • Integrate Qwen2.5-Omni

Demo

Try it Online

We have deployed a demo service on Static Badge ModelScope and 🤗 HuggingFace . The audio part is implemented using SenseVoice + Qwen-VL + CosyVoice. Now you can switch between LiteAvatar and LAM. Feel free to try it out.

Demo Video

LiteAvatar

OpenAvatarChat_Demo.mp4

LAM

OpenAvatarChat_LAM_Demo.mp4

📖 Contents

Overview

Introduction

Open Avatar Chat is a modular interactive digital human dialogue implementation that can run full functionality on a single PC. It currently supports MiniCPM-o as a multimodal language model or using cloud-based APIs to replace the conventional ASR + LLM + TTS setup. The architecture of these two modes is illustrated in the diagram below. For more pre-set modes, see below.

Requirements

  • Python version >=3.10, <3.12
  • CUDA-enabled GPU. The CUDA version supported by the NVIDIA driver needs to be >= 12.4.
  • The unquantized multimodal language model MiniCPM-o requires more than 20GB of VRAM.
  • The digital human component can perform inference using GPU/CPU. The test device is an i9-13980HX CPU, achieving up to 30 FPS for CPU inference.

Tip

Using the int4 quantized version of the language model can run on graphics cards with less than 10GB of VRAM, but quantization may affect the performance.

Replacing MiniCPM-o with cloud APIs to implement the typical ASR + LLM + TTS functions can greatly reduce configuration requirements. For more details, see ASR + LLM + TTS Mode.

Performance

In our tests, using a PC equipped with an i9-13900KF processor and Nvidia RTX 4090 graphics card, we recorded the response delay. After ten tests, the average delay was about 2.2 seconds. The delay time is the interval from the end of the user's speech to the start of the digital human's speech, including RTC two-way data transmission time, VAD (Voice Activity Detection) stop delay, and the entire process computation time.

Component Dependencies

Type Open Source Project GitHub Link Model Link
RTC HumanAIGC-Engineering/gradio-webrtc
VAD snakers4/silero-vad
LLM OpenBMB/MiniCPM-o 🤗  
LLM-int4 OpenBMB/MiniCPM-o 🤗  
Avatar HumanAIGC/lite-avatar
TTS FunAudioLLM/CosyVoice
Avatar aigc3d/LAM_Audio2Expression 🤗
facebook/wav2vec2-base-960h 🤗  
Avatar TMElyralab/MuseTalk

Pre-set Modes

CONFIG Name ASR LLM TTS AVATAR
chat_with_gs.yaml SenseVoice API API LAM
chat_with_minicpm.yaml MiniCPM-o MiniCPM-o MiniCPM-o lite-avatar
chat_with_openai_compatible.yaml SenseVoice API CosyVoice lite-avatar
chat_with_openai_compatible_edge_tts.yaml SenseVoice API edgetts lite-avatar
chat_with_openai_compatible_bailian_cosyvoice.yaml SenseVoice API API lite-avatar
chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml SenseVoice API API MuseTalk

🚀 Get Started

Before installing and deploying the corresponding mode, please refer to the installation methods for relevant modules and Optional Deployment.

Select a config

The functionalities of OpenAvatarChat will follow the config specified during startup. We provided several sample config files under the config folder.

chat_with_gs.yaml

This config uses LAM generated gaussion splatting asset as client-side rendered avatar. With api based openai compatible llm and tts from Bailian platform, only vad and asr handlers are run locally, so this is the lightest config choice, which supports multiple connection on single service.

Used Handlers
Type Handler Install Notes
Client client/h5_rendering_client/cllient_handler_lam LAM Client Rendering Handler
VAD vad/silerovad/vad_handler/silero
ASR asr/sensevoice/asr_handler_sensevoice
LLM llm/openai_compatible/llm_handler/llm_handler_openai_compatible OpenAI Compatible LLM Handler
TTS tts/bailian_tts/tts_handler_cosyvoice_bailian Bailian CosyVoice Handler
Avatar avatar/lam/avatar_handler_lam_audio2expression LAM Avatar Driver Handler

chat_with_minicpm.yaml

Use MiniCPM-o-2.6 as audio2audio chat model, it need enough VRAM and GPU computaion power.

Used Handlers
Type Handler Install Notes
Client client/rtc_client/client_handler_rtc Server Rendering RTC Client Handler
VAD vad/silerovad/vad_handler/silero
LLM llm/minicpm/llm_handler_minicpm MiniCPM Omni Speech2Speech Handler
Avatar avatar/liteavatar/avatar_handler_liteavatar LiteAvatar Avatar Handler

chat_with_openai_compatible.yaml

This config use openai-compatible api as llm provider and CosyVoice as local tts model.

Used Handlers
Type Handler Install Notes
Client client/rtc_client/client_handler_rtc Server Rendering RTC Client Handler
VAD vad/silerovad/vad_handler/silero
ASR asr/sensevoice/asr_handler_sensevoice
LLM llm/openai_compatible/llm_handler/llm_handler_openai_compatible OpenAI Compatible LLM Handler
TTS tts/cosyvoice/tts_handler_cosyvoice CosyVoice Local Inference Handler
Avatar avatar/liteavatar/avatar_handler_liteavatar LiteAvatar Avatar Handler

chat_with_openai_compatible_edge_tts.yaml

This config use Edge TTS, it does not need an API Key of Bailian.

Type Handler Install Notes
Client client/rtc_client/client_handler_rtc Server Rendering RTC Client Handler
VAD vad/silerovad/vad_handler/silero
ASR asr/sensevoice/asr_handler_sensevoice
LLM llm/openai_compatible/llm_handler/llm_handler_openai_compatible OpenAI Compatible LLM Handler
TTS tts/edgetts/tts_handler_edgetts Edge TTS Handler
Avatar avatar/liteavatar/avatar_handler_liteavatar LiteAvatar Avatar Handler

chat_with_openai_compatible_bailian_cosyvoice.yaml

Both LLM and TTS are provided by API, it is the lightest config for LiteAvatar.

Used Handlers
Type Handler Install Notes
Client client/rtc_client/client_handler_rtc Server Rendering RTC Client Handler
VAD vad/silerovad/vad_handler/silero
ASR asr/sensevoice/asr_handler_sensevoice
LLM llm/openai_compatible/llm_handler/llm_handler_openai_compatible OpenAI Compatible LLM Handler
TTS tts/bailian_tts/tts_handler_cosyvoice_bailian Bailian CosyVoice Handler
Avatar avatar/liteavatar/avatar_handler_liteavatar LiteAvatar Avatar Handler

chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml

Both LLM and TTS are provided by API, while the 2D digital human uses MuseTalk for inference. By default, it uses GPU for inference and CPU inference is not currently supported.

Used Handlers

Type Handler Install Notes
Client client/rtc_client/client_handler_rtc Server Rendering RTC Client Handler
VAD vad/silerovad/vad_handler/silero
ASR asr/sensevoice/asr_handler_sensevoice
LLM llm/openai_compatible/llm_handler/llm_handler_openai_compatible OpenAI Compatible LLM Handler
TTS tts/bailian_tts/tts_handler_cosyvoice_bailian Bailian CosyVoice Handler
Avatar avatar/musetalk/avatar_handler_musetalk MuseTalk Avatar Handler

Local Execution

Important

Submodules and dependent models in this project require the git LFS module. Please ensure that the LFS functionality is installed:

sudo apt install git-lfs
git lfs install

This project references third-party libraries via git submodules, so you need to update submodules before running:

git submodule update --init --recursive

If you encounter any issues, feel free to submit an issue to us.

This project depends on CUDA, please make sure that the CUDA version supported by the local NVIDIA driver is >= 12.4

UV Installation

It is recommended to install UV, using UV for local environment management.

Official standalone installer

# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh

PyPI installation

# With pip.
pip install uv
# Or pipx.
pipx install uv

Dependency Installation

Install all dependencies
uv sync --all-packages
Install dependencies for the required mode only
uv venv --python 3.11.11

uv pip install setuptools pip

uv run install.py --uv --config <absolute path to config file>.yaml

./scripts/post_config_install.sh --config <absolute path to config file>.yaml

Note

The post_config_install.sh script adds the NVIDIA CUDA library paths from the virtual environment to ld.so.conf.d and updates the ldconfig cache to ensure the system correctly loads these dynamic link libraries.

Run

uv run src/demo.py --config <absolute path to config file>.yaml

Docker Execution

Containerized execution: The container relies on NVIDIA's container environment. After preparing a Docker environment that supports GPUs, execute the following command to complete the construction and deployment of the image:

./build_and_run.sh --config <absolute path to config file>.yaml

Handler Dependencies Installation Notes

Server Rendering RTC Client Handler

Currently there is no extra dependency or essential configs.

LAM Client Rendering Handler

Client rendering handler is derived from Server Rendering RTC Client Handler. It supports multi-connection. Client avatar asset can be selected in handler config.

Select the Avatar Asset

LAM avatar asset can be generated by the LAM project (The ready-to-use generation pipeline is not ready yet. Stay tunned!). OpenAvatarChat provides 4 sample asset. They can be found under src/handlers/client/h5_rendering_client/lam_samples. The selected asset should be set to the asset_path field in the handler config. You can use one of the sample asset, a your own asset that created by LAM, please refer to the follow handler config sample:

LamClient:
  module: client/h5_rendering_client/client_handler_lam
  asset_path: "lam_samples/barbara.zip"
  concurrent_limit: 5

OpenAI Compatible LLM Handler

Local llm handler has relatively high startup requirements. If you already have an available LLM api_key, you can start it this way to experience interactive digital humans. Modify the corresponding config, such as the LLM_Bailian configuration in config/chat_with_openai_compatible.yaml. The invocation method in the code uses the standard OpenAI approach, which should theoretically be compatible with similar setups.

LLM_Bailian: 
  model_name: "qwen-plus"
  system_prompt: "You are an AI digital human. Respond to my questions briefly and insert punctuation where appropriate."
  api_url: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
  api_key: 'yourapikey' # default=os.getenv("DASHSCOPE_API_KEY")

Tip

OpenAvatarChat will acquire the .env file in current working directory, it is can be used to set the environment variables without change the config file.

Note

  • Internal Code Calling Method
client = OpenAI(
      api_key= self.api_key, 
      base_url=self.api_url,
  )
completion = client.chat.completions.create(
    model=self.model_name,
    messages=[
       self.system_prompt,
        {'role': 'user', 'content': chat_text}
    ],
    stream=True
    )
  • The default LLM API is Bailian api_url.

MiniCPM Omni Speech2Speech Handler

Models used

In this project, MiniCPM-o-2.6 can be used as a multimodal language model to provide dialogue capabilities for digital humans. Users can download the relevant model as needed from Huggingface or Modelscope. It is recommended to directly download the model to /models/. The default configuration points to this path, so if the model is placed elsewhere, you need to modify the configuration file. There is a corresponding model download script in the scripts directory, which can be used in a Linux environment. Please run the script in the project root directory:

scripts/download_MiniCPM-o_2.6.sh
scripts/download_MiniCPM-o_2.6-int4.sh

Note

Both full precision version and the int4 quantized one are supported. However，the int4 version need a special version of AutoGPTQ to load, refer to the model card please.

Bailian CosyVoice Handler

Bailian provides CosyVoice API, it can be used as an alternative to local tts inference handler. Though it requires an Bailian API Key, it reduces quite amount of system requirments. Sample handler config looks like this:

CosyVoice:
  module: tts/bailian_tts/tts_handler_cosyvoice_bailian
  voice: "longxiaocheng"
  model_name: "cosyvoice-v1"
  api_key: 'yourapikey' # default=os.getenv("DASHSCOPE_API_KEY")

Same as OpenAI Compatible LLM Handler, api_key can be set in the handler config or from environment variables.

Tip

OpenAvatarChat will acquire the .env file in current working directory, it is can be used to set the environment variables without change the config file.

CosyVoice Local Inference Handler

Warning

Due to an issue where the pynini package dependency fails to compile with unsupported parameters when fetched via PyPI on Windows, the current recommended workaround by CosyVoice is to install the precompiled pynini package from conda-forge on Windows using Conda.

When using CosyVoice locally as TTS on Windows, it is necessary to combine Conda and UV for installation. The specific dependency installation and execution process are as follows:

  1. Install Anaconda or Miniconda
conda create -n openavatarchat python=3.10
conda activate openavatarchat
conda install -c conda-forge pynini==2.1.6
  1. Set the environment variable indexed by UV to the Conda environment
# cmd
set VIRTUAL_ENV=%CONDA_PREFIX%
# powershell 
$env:VIRTUAL_ENV=$env:CONDA_PREFIX
  1. When installing dependencies and running with UV, add the --active parameter to prioritize the use of the activated virtual environment
# Install dependencies
uv sync --active --all-packages
# Install required dependencies only
uv run --active install.py --uv --config config/chat_with_openai_compatible.yaml
# Run CosyVoice 
uv run --active src/demo.py --config config/chat_with_openai_compatible.yaml

Note

  • TTS defaults to CosyVoice's iic/CosyVoice-300M-SFT + Chinese Female You can modify it to other models and use ref_audio_path and ref_audio_text for voice cloning.

Edge TTS Handler

OpenAvatarChat integrated Microsoft Edge TTS, it is inference on the cloud and api key is not esstential, the sample handler config looks like:

Edge_TTS:
  module: tts/edgetts/tts_handler_edgetts
  voice: "zh-CN-XiaoxiaoNeural"

LiteAvatar Avatar Handler

LiteAvatar is integarted to provide 2D avatar feature. Currenty, 100 avatar assets are provided on modelscope project LiteAvatarGallery, please refer to this project for detail. LiteAvatar can be run on CPU as well as GPU. If other GPU heavy handlers are used, let liteavatar run on cpu may be a good choice. Sample handler config looks like:

LiteAvatar:
  module: avatar/liteavatar/avatar_handler_liteavatar
  avatar_name: 20250408/sample_data
  fps: 25
  use_gpu: true

LAM Avatar Driver Handler

Models used

  • facebook/wav2vec2-base-960h 🤗
    • Download from huggingface, ensure lfs is installed properly，run following cmd under root of the project:
    git clone --depth 1 https://huggingface.co/facebook/wav2vec2-base-960h ./models/wav2vec2-base-960h
    • Download from modelscope, ensure lfs is installed properly，run following cmd under root of the project:
    git clone --depth 1 https://www.modelscope.cn/AI-ModelScope/wav2vec2-base-960h.git ./models/wav2vec2-base-960h
  • LAM_audio2exp 🤗
    • Download form huggingface, ensure lfs is installed properly，run following cmds under root of the project:
    wget https://huggingface.co/3DAIGC/LAM_audio2exp/resolve/main/LAM_audio2exp_streaming.tar -P ./models/LAM_audio2exp/
tar -xzvf ./models/LAM_audio2exp/LAM_audio2exp_streaming.tar -C ./models/LAM_audio2exp && rm ./models/LAM_audio2exp/LAM_audio2exp_streaming.tar
    • If huggingface is unreachable, it can also be downloaded from oss, run following cmds under root of the project:
    wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_streaming.tar -P ./models/LAM_audio2exp/
tar -xzvf ./models/LAM_audio2exp/LAM_audio2exp_streaming.tar -C ./models/LAM_audio2exp && rm ./models/LAM_audio2exp/LAM_audio2exp_streaming.tar

MuseTalk Avatar Handler

The project currently integrates the latest MuseTalk 1.5 (previous versions are not tested). This version supports custom avatars, which can be selected by modifying the avatar_video_path parameter.

Model Dependencies

  • MuseTalk source code includes a model download script. To keep the directory structure consistent, a modified script is provided in the scripts directory for Linux environments. The original MuseTalk code uses relative paths for loading; although adaptations have been made, some code cannot be configured via input parameters. Do not change the model download location. Run the script from the project root: 
    scripts/download_musetalk_weights.sh
  • The MuseTalk source code will download a model s3fd-619a316812.pth on first startup, which is not included in the download script. The initial download might be slow.

Configuration

  • Avatar selection: MuseTalk source includes two default avatars. You can select by modifying the avatar_video_path parameter. The system will prepare data on first load and cache it for subsequent runs. You can force regeneration by setting force_create_avatar: true. The avatar_model_dir parameter specifies where to save avatar data (default: models/musetalk/avatar_model).
  • Frame rate: Although MuseTalk documentation claims 30fps on V100, our adaptation (referencing realtime_inference.py) does not reach this in practice. We recommend fps: 20, but you can adjust based on your GPU. If you see the warning [IDLE_FRAME] Inserted idle during speaking in logs, it means actual inference fps is lower than set fps. Increasing batch_size can improve throughput, but too large a batch may slow first-frame response.

Sample config:

Avatar_MuseTalk:
  module: avatar/musetalk/avatar_handler_musetalk
  fps: 20  # Video frame rate
  batch_size: 2  # Batch processing frame count
  avatar_video_path: "src/handlers/avatar/musetalk/MuseTalk/data/video/sun.mp4"  # Initialization video path
  avatar_model_dir: "models/musetalk/avatar_model"  # Default avatar model directory
  force_create_avatar: false  # Whether to force regenerate digital human data
  debug: false  # Whether to enable debug mode
  ... # See AvatarMuseTalkConfig for more parameters

Run

  • Docker
./build_and_run.sh --config config/chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml
  • Local deployment

The order of commands for installing dependencies locally is as follows:

uv venv --python 3.11.11Add commentMore actions

./scripts/pre_config_install.sh --config config/chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml

uv run install.py --uv --config config/chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml

./scripts/post_config_install.sh --config config/chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml

Note: The mmcv installed by uv by default may report an error "No module named 'mmcv._ext'" during actual runtime. Refer to MMCV-FAQ. The solution is:

uv pip uninstall mmcv
uv pip install mmcv==2.2.0 -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.4/index.html

When running the MuseTalk source code for the first time, it will automatically download a model called s3fd-619a316812.pth. This model is now integrated into the download script. It has already been mapped when starting with Docker. However, when running locally, you need to manually perform the mapping again.

# linux
ln -s $(pwd)/models/musetalk/s3fd-619a316812/* ~/.cache/torch/hub/checkpoints/

To start the program:

uv run src/demo.py --config config/chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml

Optional Deployment

Prepare ssl certificates

Since we use rtc to stream the video and audio, if not linked from localhost an ssl certificates is needed, user can put exist ones into the ssl_certs folder and config them in the config file or create a new self signed one with the provided script. Run the script under project root to put the result into proper position.

scripts/create_ssl_certs.sh

TURN Server

If you encounter a continuous waiting state after clicking "Start Conversation", it may be due to NAT traversal issues in your deployment environment (such as deployment on cloud machines). In this case, data relay is required. On Linux systems, you can use coturn to set up a TURN server. Follow these steps to install, start, and configure coturn on the same machine:

  • Run the installation script
$ chmod 777 scripts/setup_coturn.sh
# scripts/setup_coturn.sh
  • Modify the config file, add the following configuration and start the service
default:
  chat_engine:
    handler_configs:
      RtcClient: # If using Lam, this config should be LamClient
        turn_config:
          turn_provider: "turn_server"
          urls: ["turn:your-turn-server.com:3478", "turns:your-turn-server.com:5349"]
          username: "your-username"
          credential: "your-credential"
  • Ensure that the firewall (including cloud machine security group policies) opens the ports required by coturn

Configuration

The default parameter will load config from <project_root>/configs/chat_with_minicpm.yaml. Config can be loaded from other file by add the --config parameter.

uv run src/demo.py --config <absolute-path-to-the-config>.yaml

Configurable parameters are listed here：

Parameter Default Description
log.log_level INFO Log level of the demo.
service.host 0.0.0.0 Address to start gradio application on.
service.port 8282 Port to start gradio application on.
service.cert_file ssl_certs/localhost.crt Certificate file for ssl, if both cert_file and cert_key are found, https will be enabled.
service.cert_key ssl_certs/localhost.key Certificate file for ssl, if both cert_file and cert_key are found, https will be enabled.
chat_engine.model_root models Path to find models.
chat_engine.handler_configs N/A Handler configs are provided by each handler.

Current implemented handler provide following configs:

  • VAD
Parameter Default Description
SileraVad.speaking_threshold 0.5 Threshold to determine whether user starts speaking or end speaking.
SileraVad.start_delay 2048 Speaking probability should be higher than threshold longer than this period to be recognized as start of speaking, unit in audio sample.
SileraVad.end_delay 2048 Speaking probability should be lower than threshold longer than this period to be recognized as end of speaking, unit in audio sample.
SileraVad.buffer_look_back 1024 For high threshold, the very start part to the voice may be clipped, use this to compensate, unit in audio sample.
SileraVad.speech_padding 512 Silence of this length will be padded on both start and end, unit in audio sample.
  • LLM
Parameter Default Description
S2S_MiniCPM.model_name MiniCPM-o-2_6 Which model to load, can be "MiniCPM-o-2_6" or "MiniCPM-o-2_6-int4", it should match the folder's name under model directory.
S2S_MiniCPM.voice_prompt Voice prompt for MiniCPM-o.
S2S_MiniCPM.assistant_prompt Assistant prompt for MiniCPM-o.
S2S_MiniCPM.enable_video_input False Whether video input is enabled.when video input is enbaled vram consumption will be increased largely, on 24GB gpu with non-quantized model, oom may occur during inference.
S2S_MiniCPM.skip_video_frame -1 Decide how many frames will be used when video modality is used. -1 means only the latest frame in every 1 second interval will be used. 0 means all frames will be used. n>0 means n frames will be skipped after each accepted frame.

ASR FunASR Model

Parameter Default Value Description
ASR_Funasr.model_name iic/SenseVoiceSmall This parameter selects a model from FunASR. Models are downloaded automatically. To use a local model, provide an absolute path.

LLM Plain Text Model

Parameter Default Value Description
LLM_Bailian.model_name qwen-plus The API for Bailian's testing environment. Free quotas can be obtained from Bailian.
LLM_Bailian.system_prompt Default system prompt
LLM_Bailian.api_url API URL for the model
LLM_Bailian.api_key API key for the model

TTS CosyVoice Model

Parameter Default Value Description
TTS_CosyVoice.api_url Required if deploying CosyVoice server on another machine.
TTS_CosyVoice.model_name Refer to CosyVoice for details.
TTS_CosyVoice.spk_id '中文女' Use official SFT voices like '英文女' or '英文男'. Mutually exclusive with ref_audio_path.
TTS_CosyVoice.ref_audio_path Absolute path to the reference audio. Mutually exclusive with spk_id.
TTS_CosyVoice.ref_audio_text Text content of the reference audio.
TTS_CosyVoice.sample_rate 24000 Output audio sample rate

LiteAvatar Digital Human

Parameter Default Value Description
LiteAvatar.avatar_name 20250408/sample_data Name of the digital human data. 100 avatars provided on ModelScope. Refer to LiteAvatarGallery for more details.
LiteAvatar.fps 25 Frame rate for the digital human. On high-performance CPUs, it can be set to 30 FPS.
LiteAvatar.enable_fast_mode False Low-latency mode. Enabling this reduces response delay but may cause stuttering at the beginning of responses on underpowered systems.
LiteAvatar.use_gpu True Whether to use GPU acceleration. CUDA backend for now.

Important

All path parameters in the configuration can use either absolute paths or paths relative to the project root directory.

FAQ

Frequently asked questions encountered during the course of the project can be found at link

Community Thanks

Star History

Citation

If you found OpenAvatarChat helpful in your research/project, we would appreciate a Star⭐ and citation✏️

@software{avatarchat2025,
  author = {Gang Cheng, Tao Chen, Feng Wang, Binchao Huang, Hui Xu, Guanqiao He, Yi Lu, Shengyin Tan},
  title = {OpenAvatarChat},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/HumanAIGC-Engineering/OpenAvatarChat}
}

About

No description, website, or topics provided.

Resources

Readme

License

Apache-2.0 license
Activity

Stars

1.4k stars

Watchers

21 watching

Forks

210 forks
Report repository

Releases 5

0.4.1 Latest
Jun 12, 2025
+ 4 releases

Packages

No packages published

Contributors 10

Languages