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Retrieval-based-Voice-Conversion-WebUI
-一个基于VITS的简单易用的语音转换(变声器)框架
-
-[](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI)
-
-

-
-[](https://colab.research.google.com/github/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb)
-[](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/%E4%BD%BF%E7%94%A8%E9%9C%80%E9%81%B5%E5%AE%88%E7%9A%84%E5%8D%8F%E8%AE%AE-LICENSE.txt)
-[](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)
-
-[](https://discord.gg/HcsmBBGyVk)
-
-[**更新日志**](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/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))
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-
-[**English**](./docs/README.en.md) | [**中文简体**](./README.md) | [**日本語**](./docs/README.ja.md) | [**한국어**](./docs/README.ko.md) ([**韓國語**](./docs/README.ko.han.md))
-
-:fire: 使用了RVC变声器训练的人声转木吉他模型在线demo:fire: :https://huggingface.co/spaces/lj1995/vocal2guitar
-
-:fire: RVC人声转吉他效果展示视频:fire: :https://www.bilibili.com/video/BV19W4y1D7tT/
-
-> 点此查看我们的[演示视频](https://www.bilibili.com/video/BV1pm4y1z7Gm/) !
-
-> 使用了RVC的实时语音转换: [w-okada/voice-changer](https://github.com/w-okada/voice-changer)
-
-> 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
-
-> 后续会陆续加入高质量有授权歌声训练集训练底模
-
-## 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
-
-## 环境配置
-推荐使用poetry配置环境。
-
-以下指令需在Python版本大于3.8的环境中执行:
-```bash
-# 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/
-pip install torch torchvision torchaudio
-
-#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
-#pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
-
-# 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
-curl -sSL https://install.python-poetry.org | python3 -
-
-# 通过poetry安装依赖
-poetry install
-```
-
-你也可以通过pip来安装依赖:
-```bash
+```sh
+virtualenv env
+source env/bin/activate
pip install -r requirements.txt
+
+python3 fakeyou_infer-v2.py 0 "Path/to/the/input/audio file/Chug_Jug.wav" "Path/to/the/index file/added_IVF2933_Flat_nprobe_10.index" harvest "Path/to/the/output audio file/test_v2.wav" "Path/to/the/trained model/biggie-smalls.pth" 0.66 cuda:0 True 3 0 1 0.33
+
+Example:
+
+python3 fakeyou_infer-v2.py 0 "E:\codes\py39\RVC-beta\todo-songs\1111.wav" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "test_v2.wav" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
+
+
+
```
-## 其他预模型准备
-RVC需要其他一些预模型来推理和训练。
+Each argument passed to the script is used as follows:
-你可以从我们的[Hugging Face space](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)下载到这些模型。
+`f0up_key`: The f0 key for the input audio file.
+`input_path`: Path to the input audio file.
+`index_path`: Path to the index file.
+`f0method`: F0 estimation method to be used ('harvest' or 'pm').
+`opt_path`: Path to the output audio file.
+`model_path`: Path to the trained model.
+`index_rate`: The rate for the index.
+`device`: The computation device to be used ('cuda:0' for GPU or 'cpu' for CPU).
+`is_half`: Boolean value indicating whether to use half precision. Use 'True' for half precision and 'False' for full precision.
+`filter_radius`: The radius of the filter.
+`resample_sr`: The sample rate for resampling.
+`rms_mix_rate`: The RMS mix rate.
+`protect`: The protect value.
-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:
-```bash
-hubert_base.pt
-
-./pretrained
-
-./uvr5_weights
-
-想测试v2版本模型的话(v2版本模型将特征从 9层hubert+final_proj的256维输入 变更为 12层hubert的768维输入,并且增加了3个周期鉴别器),需要额外下载
-
-./pretrained_v2
-
-#如果你正在使用Windows,则你可能需要这个文件,若ffmpeg和ffprobe已安装则跳过; ubuntu/debian 用户可以通过apt install ffmpeg来安装这2个库
-./ffmpeg
-
-./ffprobe
-```
-之后使用以下指令来启动WebUI:
-```bash
-python infer-web.py
-```
-如果你正在使用Windows,你可以直接下载并解压`RVC-beta.7z`,运行`go-web.bat`以启动WebUI。
-
-仓库内还有一份`小白简易教程.doc`以供参考。
-
-## 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)
-
-## 感谢所有贡献者作出的努力
-
-
-
+See `fakeyou_infer-v2.py` for more info