AI prompts
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/)
## 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)
## 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rtmp和webrtc
6. 支持视频编排：不说话时播放自定义视频
7. 支持多并发
## 1. Installation
Tested on Ubuntu 20.04, Python3.10, Pytorch 1.12 and CUDA 11.3
### 1.1 Install dependency
```bash
conda create -n nerfstream python=3.10
conda activate nerfstream
#如果cuda版本不为11.3(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==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
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/en/latest/faq.html)
linux cuda环境搭建可以参考这篇文章 https://zhuanlan.zhihu.com/p/674972886
## 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
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
<font color=red>服务端需要开放端口 tcp:8010; udp:1-65536 </font>
如果需要商用高清wav2lip模型，[链接](https://livetalking-doc.readthedocs.io/zh-cn/latest/service.html#wav2lip)
- 快速体验
<https://www.compshare.cn/images-detail?ImageID=compshareImage-18tpjhhxoq3j&referral_code=3XW3852OBmnD089hMMrtuU&ytag=GPU_GitHub_livetalking1.3> 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-detail?ImageID=compshareImage-18tpjhhxoq3j&referral_code=3XW3852OBmnD089hMMrtuU&ytag=GPU_livetalking1.3>
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. TODO
- [x] 添加chatgpt实现数字人对话
- [x] 声音克隆
- [x] 数字人静音时用一段视频代替
- [x] MuseTalk
- [x] Wav2Lip
- [x] Ultralight-Digital-Human
---
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":"9603","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"