base on Easily train a good VC model with voice data <= 10 mins! <div align="center"> <h1>Retrieval-based-Voice-Conversion-WebUI</h1> 一个基于VITS的简单易用的变声框架<br><br> [](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) <img src="https://counter.seku.su/cmoe?name=rvc&theme=r34" /><br> [](https://colab.research.google.com/github/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb) [](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/LICENSE) [](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/) [](https://discord.gg/HcsmBBGyVk) [**更新日志**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/docs/Changelog_CN.md) | [**常见问题解答**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98%E8%A7%A3%E7%AD%94) | [**AutoDL·5毛钱训练AI歌手**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/Autodl%E8%AE%AD%E7%BB%83RVC%C2%B7AI%E6%AD%8C%E6%89%8B%E6%95%99%E7%A8%8B) | [**对照实验记录**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/Autodl%E8%AE%AD%E7%BB%83RVC%C2%B7AI%E6%AD%8C%E6%89%8B%E6%95%99%E7%A8%8B](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/%E5%AF%B9%E7%85%A7%E5%AE%9E%E9%AA%8C%C2%B7%E5%AE%9E%E9%AA%8C%E8%AE%B0%E5%BD%95)) | [**在线演示**](https://modelscope.cn/studios/FlowerCry/RVCv2demo) [**English**](./docs/en/README.en.md) | [**中文简体**](./README.md) | [**日本語**](./docs/jp/README.ja.md) | [**한국어**](./docs/kr/README.ko.md) ([**韓國語**](./docs/kr/README.ko.han.md)) | [**Français**](./docs/fr/README.fr.md) | [**Türkçe**](./docs/tr/README.tr.md) | [**Português**](./docs/pt/README.pt.md) </div> > 底模使用接近50小时的开源高质量VCTK训练集训练，无版权方面的顾虑，请大家放心使用 > 请期待RVCv3的底模，参数更大，数据更大，效果更好，基本持平的推理速度，需要训练数据量更少。 <table> <tr> <td align="center">训练推理界面</td> <td align="center">实时变声界面</td> </tr> <tr> <td align="center"><img src="https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/assets/129054828/092e5c12-0d49-4168-a590-0b0ef6a4f630"></td> <td align="center"><img src="https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/assets/129054828/730b4114-8805-44a1-ab1a-04668f3c30a6"></td> </tr> <tr> <td align="center">go-web.bat</td> <td align="center">go-realtime-gui.bat</td> </tr> <tr> <td align="center">可以自由选择想要执行的操作。</td> <td align="center">我们已经实现端到端170ms延迟。如使用ASIO输入输出设备，已能实现端到端90ms延迟，但非常依赖硬件驱动支持。</td> </tr> </table> ## 简介 本仓库具有以下特点 + 使用top1检索替换输入源特征为训练集特征来杜绝音色泄漏 + 即便在相对较差的显卡上也能快速训练 + 使用少量数据进行训练也能得到较好结果(推荐至少收集10分钟低底噪语音数据) + 可以通过模型融合来改变音色(借助ckpt处理选项卡中的ckpt-merge) + 简单易用的网页界面 + 可调用UVR5模型来快速分离人声和伴奏 + 使用最先进的[人声音高提取算法InterSpeech2023-RMVPE](#参考项目)根绝哑音问题。效果最好（显著地）但比crepe_full更快、资源占用更小 + A卡I卡加速支持 点此查看我们的[演示视频](https://www.bilibili.com/video/BV1pm4y1z7Gm/) ! ## 环境配置 以下指令需在 Python 版本大于3.8的环境中执行。 ### Windows/Linux/MacOS等平台通用方法 下列方法任选其一。 #### 1. 通过 pip 安装依赖 1. 安装Pytorch及其核心依赖，若已安装则跳过。参考自: https://pytorch.org/get-started/locally/ ```bash pip install torch torchvision torchaudio ``` 2. 如果是 win 系统 + Nvidia Ampere 架构(RTX30xx)，根据 #21 的经验，需要指定 pytorch 对应的 cuda 版本 ```bash pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 ``` 3. 根据自己的显卡安装对应依赖 - N卡 ```bash pip install -r requirements.txt ``` - A卡/I卡 ```bash pip install -r requirements-dml.txt ``` - A卡ROCM(Linux) ```bash pip install -r requirements-amd.txt ``` - I卡IPEX(Linux) ```bash pip install -r requirements-ipex.txt ``` #### 2. 通过 poetry 来安装依赖 安装 Poetry 依赖管理工具，若已安装则跳过。参考自: https://python-poetry.org/docs/#installation ```bash curl -sSL https://install.python-poetry.org | python3 - ``` 通过 Poetry 安装依赖时，python 建议使用 3.7-3.10 版本，其余版本在安装 llvmlite==0.39.0 时会出现冲突 ```bash poetry init -n poetry env use "path to your python.exe" poetry run pip install -r requirments.txt ``` ### MacOS 可以通过 `run.sh` 来安装依赖 ```bash sh ./run.sh ``` ## 其他预模型准备 RVC需要其他一些预模型来推理和训练。 你可以从我们的[Hugging Face space](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)下载到这些模型。 ### 1. 下载 assets 以下是一份清单，包括了所有RVC所需的预模型和其他文件的名称。你可以在`tools`文件夹找到下载它们的脚本。 - ./assets/hubert/hubert_base.pt - ./assets/pretrained - ./assets/uvr5_weights 想使用v2版本模型的话，需要额外下载 - ./assets/pretrained_v2 ### 2. 安装 ffmpeg 若ffmpeg和ffprobe已安装则跳过。 #### Ubuntu/Debian 用户 ```bash sudo apt install ffmpeg ``` #### MacOS 用户 ```bash brew install ffmpeg ``` #### Windows 用户 下载后放置在根目录。 - 下载[ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) - 下载[ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) ### 3. 下载 rmvpe 人声音高提取算法所需文件 如果你想使用最新的RMVPE人声音高提取算法，则你需要下载音高提取模型参数并放置于RVC根目录。 - 下载[rmvpe.pt](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/rmvpe.pt) #### 下载 rmvpe 的 dml 环境(可选, A卡/I卡用户) - 下载[rmvpe.onnx](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/rmvpe.onnx) ### 4. AMD显卡Rocm(可选, 仅Linux) 如果你想基于AMD的Rocm技术在Linux系统上运行RVC，请先在[这里](https://rocm.docs.amd.com/en/latest/deploy/linux/os-native/install.html)安装所需的驱动。 若你使用的是Arch Linux，可以使用pacman来安装所需驱动： ```` pacman -S rocm-hip-sdk rocm-opencl-sdk ```` 对于某些型号的显卡，你可能需要额外配置如下的环境变量（如：RX6700XT）： ```` export ROCM_PATH=/opt/rocm export HSA_OVERRIDE_GFX_VERSION=10.3.0 ```` 同时确保你的当前用户处于`render`与`video`用户组内： ```` sudo usermod -aG render $USERNAME sudo usermod -aG video $USERNAME ```` ## 开始使用 ### 直接启动 使用以下指令来启动 WebUI ```bash python infer-web.py ``` 若先前使用 Poetry 安装依赖，则可以通过以下方式启动WebUI ```bash poetry run python infer-web.py ``` ### 使用整合包 下载并解压`RVC-beta.7z` #### Windows 用户 双击`go-web.bat` #### MacOS 用户 ```bash sh ./run.sh ``` ### 对于需要使用IPEX技术的I卡用户(仅Linux) ```bash source /opt/intel/oneapi/setvars.sh ``` ## 参考项目 + [ContentVec](https://github.com/auspicious3000/contentvec/) + [VITS](https://github.com/jaywalnut310/vits) + [HIFIGAN](https://github.com/jik876/hifi-gan) + [Gradio](https://github.com/gradio-app/gradio) + [FFmpeg](https://github.com/FFmpeg/FFmpeg) + [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui) + [audio-slicer](https://github.com/openvpi/audio-slicer) + [Vocal pitch extraction:RMVPE](https://github.com/Dream-High/RMVPE) + The pretrained model is trained and tested by [yxlllc](https://github.com/yxlllc/RMVPE) and [RVC-Boss](https://github.com/RVC-Boss). ## 感谢所有贡献者作出的努力 <a href="https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/graphs/contributors" target="_blank"> <img src="https://contrib.rocks/image?repo=RVC-Project/Retrieval-based-Voice-Conversion-WebUI" /> </a> ", Assign "at most 3 tags" to the expected json: {"id":"2293","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"