
VideoChat
实时语音交互数字人,支持端到端语音方案(GLM-4-Voice - THG)和级联方案(ASR-LLM-TTS-THG)。可自定义形象与音色,无须训练,支持音色克隆,首包延迟低至3s。Real-time voice interactive digital human, supporting end-to-end voice solutions (GLM-4-Voice - THG) and cascaded solutions (ASR-LLM-TTS-THG). Customizable appearance and voice, supporting voice cloning, with initial package delay as low as 3s.
Stars: 811

VideoChat is a real-time voice interaction digital human tool that supports end-to-end voice solutions (GLM-4-Voice - THG) and cascade solutions (ASR-LLM-TTS-THG). Users can customize appearance and voice, support voice cloning, and achieve low first-packet delay of 3s. The tool offers various modules such as ASR, LLM, MLLM, TTS, and THG for different functionalities. It requires specific hardware and software configurations for local deployment, and provides options for weight downloads and customization of digital human appearance and voice. The tool also addresses known issues related to resource availability, video streaming optimization, and model loading.
README:
实时语音交互数字人,支持端到端语音方案(GLM-4-Voice - THG)和级联方案(ASR-LLM-TTS-THG)。可自定义形象与音色,支持音色克隆,首包延迟低至3s。
在线demo:https://www.modelscope.cn/studios/AI-ModelScope/video_chat
详细的技术介绍请看这篇文章
中文简体 | English
- [x] TTS模块添加音色克隆功能
- [x] TTS模块添加edge-tts
- [x] LLM模块添加qwen本地推理
- [x] 支持GLM-4-Voice,提供ASR-LLM-TTS-THG和MLLM-THG两种生成方式
- [ ] GLM-4-Voice集成vllm推理加速
- [ ] 集成gradio-webrtc(需等待支持音视频同步),提高视频流稳定性
- ASR (Automatic Speech Recognition): FunASR
- LLM (Large Language Model): Qwen
- End-to-end MLLM (Multimodal Large Language Model): GLM-4-Voice
- TTS (Text to speech): GPT-SoVITS, CosyVoice, edge-tts
- THG (Talking Head Generation): MuseTalk
-
级联方案(ASR-LLM-TTS-THG):约8G,首包约3s(单张A100)。
-
端到端语音方案(MLLM-THG):约20G,首包约7s(单张A100)。
对于不需要使用端到端 MLLM 的开发者,可以选择仅包含级联方案的cascade_only
分支。
$ git checkout cascade_only
- ubuntu 22.04
- python 3.10
- CUDA 12.2
- torch 2.3.0
$ git lfs install
$ git clone https://www.modelscope.cn/studios/AI-ModelScope/video_chat.git
$ conda create -n metahuman python=3.10
$ conda activate metahuman
$ cd video_chat
$ pip install -r requirements.txt
创空间仓库已设置git lfs
追踪权重文件,如果是通过git clone https://www.modelscope.cn/studios/AI-ModelScope/video_chat.git
克隆,则无需额外配置
参考这个链接
目录如下:
./weights/
├── dwpose
│ └── dw-ll_ucoco_384.pth
├── face-parse-bisent
│ ├── 79999_iter.pth
│ └── resnet18-5c106cde.pth
├── musetalk
│ ├── musetalk.json
│ └── pytorch_model.bin
├── sd-vae-ft-mse
│ ├── config.json
│ └── diffusion_pytorch_model.bin
└── whisper
└── tiny.pt
参考这个链接
在app.py
中添加如下代码即可完成下载。
from modelscope import snapshot_download
snapshot_download('ZhipuAI/glm-4-voice-tokenizer',cache_dir='./weights')
snapshot_download('ZhipuAI/glm-4-voice-decoder',cache_dir='./weights')
snapshot_download('ZhipuAI/glm-4-voice-9b',cache_dir='./weights')
LLM模块和TTS模块提供了多种方式,可自行选择推理方式
对于LLM模块和TTS模块,如果本地机器性能有限,可使用阿里云大模型服务平台百炼提供的Qwen API和CosyVoice API,请在app.py(line 14)中配置API-KEY。
参考这个链接完成API-KEY的获取与配置。
os.environ["DASHSCOPE_API_KEY"] = "INPUT YOUR API-KEY HERE"
如果不使用API-KEY,请参考以下说明修改相关代码。
src/llm.py
中提供了Qwen
和Qwen_API
两个类分别处理本地推理和调用API。若不使用API-KEY,有以下两种方式进行本地推理:
- 使用
Qwen
完成本地推理。 -
Qwen_API
默认调用API完成推理,若不使用API-KEY,还可以使用vLLM
加速LLM推理。可参考如下方式安装vLLM
:安装完成后,参考这个链接进行部署,使用$ git clone https://github.com/vllm-project/vllm.git $ cd vllm $ python use_existing_torch.py $ pip install -r requirements-build.txt $ pip install -e . --no-build-isolation
Qwen_API(api_key="EMPTY",base_url="http://localhost:8000/v1")
初始化实例调用本地推理服务。
src/tts.py
中提供了GPT_SoVits_TTS
和CosyVoice_API
分别处理本地推理和调用API。若不使用API-KEY,可直接删除CosyVoice_API
相关的内容,使用Edge_TTS
调用Edge浏览器的免费TTS服务进行推理。
$ python app.py
- 在
/data/video/
中添加录制好的数字人形象视频 - 修改
/src/thg.py
中Muse_Talk
类的avatar_list
,加入(形象名, bbox_shfit)
,关于bbox_shift的说明参考这个链接 - 在
/app.py
中Gradio的avatar_name
中加入数字人形象名后重新启动服务,等待完成初始化即可。
GPT-SoVits
支持自定义音色。demo中可使用音色克隆功能,上传任意语音内容的参考音频后开始对话,或将音色永久添加到demo中:
- 在
/data/audio
中添加音色参考音频,音频长度3-10s,命名格式为x.wav
- 在
/app.py
中Gradio的avatar_voice
中加入音色名(命名格式为x (GPT-SoVits)
)后重新启动服务。 - TTS选型选择
GPT-SoVits
,开始对话
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