base on Real time interactive streaming digital human [English](./README-EN.md) | 中文版 实时交互流式数字人，实现音视频同步对话。基本可以达到商用效果 [wav2lip效果](https://www.bilibili.com/video/BV1scwBeyELA/) | [ernerf效果](https://www.bilibili.com/video/BV1G1421z73r/) | [musetalk效果](https://www.bilibili.com/video/BV1gm421N7vQ/) 国内镜像地址:<https://gitee.com/lipku/LiveTalking> ## 为避免与3d数字人混淆，原项目metahuman-stream改名为livetalking，原有链接地址继续可用 ## News - 2024.12.8 完善多并发，显存不随并发数增加 - 2024.12.21 添加wav2lip、musetalk模型预热，解决第一次推理卡顿问题。感谢[@heimaojinzhangyz](https://github.com/heimaojinzhangyz) - 2024.12.28 添加数字人模型Ultralight-Digital-Human。 感谢[@lijihua2017](https://github.com/lijihua2017) - 2025.2.7 添加fish-speech tts - 2025.2.21 添加wav2lip256开源模型 感谢@不蠢不蠢 - 2025.3.2 添加腾讯语音合成服务 - 2025.3.16 支持mac gpu推理，感谢[@GcsSloop](https://github.com/GcsSloop) - 2025.5.1 精简运行参数，ernerf模型移至git分支ernerf-rtmp - 2025.6.7 添加虚拟摄像头输出 ## Features 1. 支持多种数字人模型: ernerf、musetalk、wav2lip、Ultralight-Digital-Human 2. 支持声音克隆 3. 支持数字人说话被打断 4. 支持全身视频拼接 5. 支持webrtc、虚拟摄像头输出 6. 支持动作编排：不说话时播放自定义视频 7. 支持多并发 ## 1. Installation Tested on Ubuntu 24.04, Python3.10, Pytorch 2.5.0 and CUDA 12.4 ### 1.1 Install dependency ```bash conda create -n nerfstream python=3.10 conda activate nerfstream #如果cuda版本不为12.4(运行nvidia-smi确认版本)，根据<https://pytorch.org/get-started/previous-versions/>安装对应版本的pytorch conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=12.4 -c pytorch -c nvidia pip install -r requirements.txt #如果需要训练ernerf模型，安装下面的库 # pip install "git+https://github.com/facebookresearch/pytorch3d.git" # pip install tensorflow-gpu==2.8.0 # pip install --upgrade "protobuf<=3.20.1" ``` 安装常见问题[FAQ](https://livetalking-doc.readthedocs.io/zh-cn/latest/faq.html) linux cuda环境搭建可以参考这篇文章 <https://zhuanlan.zhihu.com/p/674972886> 视频连不上解决方法 <https://mp.weixin.qq.com/s/MVUkxxhV2cgMMHalphr2cg> ## 2. Quick Start - 下载模型 夸克云盘<https://pan.quark.cn/s/83a750323ef0> GoogleDriver <https://drive.google.com/drive/folders/1FOC_MD6wdogyyX_7V1d4NDIO7P9NlSAJ?usp=sharing> 将wav2lip256.pth拷到本项目的models下, 重命名为wav2lip.pth; 将wav2lip256_avatar1.tar.gz解压后整个文件夹拷到本项目的data/avatars下 - 运行 python app.py --transport webrtc --model wav2lip --avatar_id wav2lip256_avatar1 <font color=red>服务端需要开放端口 tcp:8010; udp:1-65536 </font> 客户端可以选用以下两种方式: (1)用浏览器打开http://serverip:8010/webrtcapi.html , 先点‘start',播放数字人视频；然后在文本框输入任意文字，提交。数字人播报该段文字 (2)用客户端方式, 下载地址<https://pan.quark.cn/s/d7192d8ac19b> - 快速体验 <https://www.compshare.cn/images/4458094e-a43d-45fe-9b57-de79253befe4?referral_code=3XW3852OBmnD089hMMrtuU&ytag=GPU_GitHub_livetalking> 用该镜像创建实例即可运行成功 如果访问不了huggingface，在运行前 ``` export HF_ENDPOINT=https://hf-mirror.com ``` ## 3. More Usage 使用说明: <https://livetalking-doc.readthedocs.io/> ## 4. Docker Run 不需要前面的安装，直接运行。 ``` docker run --gpus all -it --network=host --rm registry.cn-beijing.aliyuncs.com/codewithgpu2/lipku-metahuman-stream:2K9qaMBu8v ``` 代码在/root/metahuman-stream，先git pull拉一下最新代码，然后执行命令同第2、3步 提供如下镜像 - autodl镜像: <https://www.codewithgpu.com/i/lipku/metahuman-stream/base> [autodl教程](https://livetalking-doc.readthedocs.io/en/latest/autodl/README.html) - ucloud镜像: <https://www.compshare.cn/images/4458094e-a43d-45fe-9b57-de79253befe4?referral_code=3XW3852OBmnD089hMMrtuU&ytag=GPU_GitHub_livetalking> 可以开放任意端口，不需要另外部署srs服务. [ucloud教程](https://livetalking-doc.readthedocs.io/en/latest/ucloud/ucloud.html) ## 5. 性能 - 性能主要跟cpu和gpu相关，每路视频压缩需要消耗cpu，cpu性能与视频分辨率正相关；每路口型推理跟gpu性能相关。 - 不说话时的并发数跟cpu相关，同时说话的并发数跟gpu相关。 - 后端日志inferfps表示显卡推理帧率，finalfps表示最终推流帧率。两者都要在25以上才能实时。如果inferfps在25以上，finalfps达不到25表示cpu性能不足。 - 实时推理性能 模型 |显卡型号 |fps :---- |:--- |:--- wav2lip256 | 3060 | 60 musetalk | 3080Ti | 45 wav2lip256 | 3080Ti | 120 wav2lip256显卡3060以上即可，musetalk需要3080Ti以上。 ## 6. 商业版 提供如下扩展功能，适用于对开源项目已经比较熟悉，需要扩展产品功能的用户 1. 高清wav2lip模型 2. 完全语音交互，数字人回答过程中支持通过唤醒词或者按钮打断提问 3. 实时同步字幕，给前端提供数字人每句话播报开始、结束事件 4. 每个连接可以指定对应avatar和音色，avatar图片加载加速 5. 动作编排：不说话时动作、唤醒时动作、思考时动作、进入休眠动作 6. 支持不限时长的数字人形象avatar 7. 提供实时音频流输入接口 8. 数字人透明背景，能叠加动态背景 ## 7. 声明 基于本项目开发并发布在B站、视频号、抖音等网站上的视频需带上LiveTalking水印和标识，如需去除请联系作者备案授权。 --- 如果本项目对你有帮助，帮忙点个star。也欢迎感兴趣的朋友一起来完善该项目. * 知识星球: https://t.zsxq.com/7NMyO 沉淀高质量常见问题、最佳实践经验、问题解答 * 微信公众号：数字人技术  ", Assign "at most 3 tags" to the expected json: {"id":"12565","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"