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<div align="center">
<h1>GPT-SoVITS-WebUI</h1>
A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
强大的少样本语音转换与语音合成Web用户界面。<br><br>
[![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange)](https://github.com/RVC-Boss/GPT-SoVITS)
@ -11,118 +11,60 @@ A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
[![Licence](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/🤗%20-Models%20Repo-yellow.svg?style=for-the-badge)](https://huggingface.co/lj1995/GPT-SoVITS/tree/main)
[**English**](./README.md) | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md)
[**English**](../../README.md) | [**中文简体**](./README.md) | [**日本語**](../ja/README.md) | [**한국어**](../ko/README.md)
</div>
---
> Check out our [demo video](https://www.bilibili.com/video/BV12g4y1m7Uw) here!
Unseen speakers few-shot fine-tuning demo:
> 查看我们的介绍视频 [demo video](https://www.bilibili.com/video/BV12g4y1m7Uw)
https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-80c060ab47fb
For users in China region, you can use AutoDL Cloud Docker to experience the full functionality online: https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official
中国地区用户可使用 AutoDL 云端镜像进行体验：https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official
## Features:
## 功能：
1. **Zero-shot TTS:** Input a 5-second vocal sample and experience instant text-to-speech conversion.
1. **零样本文本到语音（TTS）：** 输入 5 秒的声音样本，即刻体验文本到语音转换。
2. **Few-shot TTS:** Fine-tune the model with just 1 minute of training data for improved voice similarity and realism.
2. **少样本 TTS：** 仅需 1 分钟的训练数据即可微调模型，提升声音相似度和真实感。
3. **Cross-lingual Support:** Inference in languages different from the training dataset, currently supporting English, Japanese, and Chinese.
3. **跨语言支持：** 支持与训练数据集不同语言的推理，目前支持英语、日语和中文。
4. **WebUI Tools:** Integrated tools include voice accompaniment separation, automatic training set segmentation, Chinese ASR, and text labeling, assisting beginners in creating training datasets and GPT/SoVITS models.
4. **WebUI 工具：** 集成工具包括声音伴奏分离、自动训练集分割、中文自动语音识别(ASR)和文本标注，协助初学者创建训练数据集和 GPT/SoVITS 模型。
## Environment Preparation
## 环境准备
If you are a Windows user (tested with win>=10) you can install directly via the prezip. Just download the [prezip](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true), unzip it and double-click go-webui.bat to start GPT-SoVITS-WebUI.
如果你是 Windows 用户（已在 win>=10 上测试），可以直接通过预打包文件安装。只需下载[预打包文件](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true)，解压后双击 go-webui.bat 即可启动 GPT-SoVITS-WebUI。
### Tested Environments
### 测试通过的 Python 和 PyTorch 版本
- Python 3.9, PyTorch 2.0.1, CUDA 11
- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
- Python 3.9, PyTorch 2.3.0.dev20240122, macOS 14.3 (Apple silicon, GPU)
- Python 3.9、PyTorch 2.0.1 和 CUDA 11
- Python 3.10.13, PyTorch 2.1.2 和 CUDA 12.3
- Python 3.9、Pytorch 2.3.0.dev20240122 和 macOS 14.3（Apple 芯片，GPU）
_Note: numba==0.56.4 require py<3.11_
_注意: numba==0.56.4 需要 python<3.11_
### Quick Install with Conda
### Mac 用户
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
bash install.sh
```
如果你是 Mac 用户，请先确保满足以下条件以使用 GPU 进行训练和推理：
### Install Manually
- 搭载 Apple 芯片或 AMD GPU 的 Mac
- macOS 12.3 或更高版本
- 已通过运行`xcode-select --install`安装 Xcode command-line tools
#### Pip Packages
_其他 Mac 仅支持使用 CPU 进行推理_
```bash
pip install -r requirements.txt
```
然后使用以下命令安装：
#### FFmpeg
##### Conda Users
```bash
conda install ffmpeg
```
##### Ubuntu/Debian Users
```bash
sudo apt install ffmpeg
sudo apt install libsox-dev
conda install -c conda-forge 'ffmpeg<7'
```
##### MacOS Users
```bash
brew install ffmpeg
```
##### Windows Users
Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root.
### Pretrained Models
Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) and place them in `tools/uvr5/uvr5_weights`.
Users in China region can download these two models by entering the links below and clicking "Download a copy"
- [GPT-SoVITS Models](https://www.icloud.com.cn/iclouddrive/056y_Xog_HXpALuVUjscIwTtg#GPT-SoVITS_Models)
- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/damo_asr/models`.
### For Mac Users
If you are a Mac user, make sure you meet the following conditions for training and inferencing with GPU:
- Mac computers with Apple silicon or AMD GPUs
- macOS 12.3 or later
- Xcode command-line tools installed by running `xcode-select --install`
_Other Macs can do inference with CPU only._
Then install by using the following commands:
#### Create Environment
#### 创建环境
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
```
#### Install Requirements
#### 安装依赖
```bash
pip install -r requirements.txt
@ -130,76 +72,132 @@ pip uninstall torch torchaudio
pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
```
### Using Docker
### 使用 Conda 快速安装
#### docker-compose.yaml configuration
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
bash install.sh
```
0. Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs.
1. Environment Variables：
### 手动安装包
- is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
#### Pip 包
2. Volumes Configuration，The application's root directory inside the container is set to /workspace. The default docker-compose.yaml lists some practical examples for uploading/downloading content.
3. shm_size： The default available memory for Docker Desktop on Windows is too small, which can cause abnormal operations. Adjust according to your own situation.
4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
```bash
pip install -r requirements.txt
```
#### Running with docker compose
#### FFmpeg
##### Conda 使用者
```bash
conda install ffmpeg
```
##### Ubuntu/Debian 使用者
```bash
sudo apt install ffmpeg
sudo apt install libsox-dev
conda install -c conda-forge 'ffmpeg<7'
```
##### MacOS 使用者
```bash
brew install ffmpeg
```
##### Windows 使用者
下载并将 [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) 和 [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) 放置在 GPT-SoVITS 根目录下。
### 在 Docker 中使用
#### docker-compose.yaml 设置
0. image 的标签：由于代码库更新很快，镜像的打包和测试又很慢，所以请自行在 [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) 查看当前打包好的最新的镜像并根据自己的情况选用，或者在本地根据您自己的需求通过 Dockerfile 进行构建。
1. 环境变量：
- is_half: 半精度/双精度控制。在进行 "SSL extracting" 步骤时如果无法正确生成 4-cnhubert/5-wav32k 目录下的内容时，一般都是它引起的，可以根据实际情况来调整为 True 或者 False。
2. Volume 设置，容器内的应用根目录设置为 /workspace。 默认的 docker-compose.yaml 中列出了一些实际的例子，便于上传/下载内容。
3. shm_size：Windows 下的 Docker Desktop 默认可用内存过小，会导致运行异常，根据自己情况酌情设置。
4. deploy 小节下的 gpu 相关内容，请根据您的系统和实际情况酌情设置。
#### 通过 docker compose 运行
```
docker compose -f "docker-compose.yaml" up -d
```
#### Running with docker command
#### 通过 docker 命令运行
As above, modify the corresponding parameters based on your actual situation, then run the following command:
同上，根据您自己的实际情况修改对应的参数，然后运行如下命令：
```
docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9870:9870 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:xxxxx
```
## Dataset Format
### 预训练模型
The TTS annotation .list file format:
从 [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) 下载预训练模型，并将它们放置在 `GPT_SoVITS\pretrained_models` 中。
对于 UVR5（人声/伴奏分离和混响移除，另外），从 [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) 下载模型，并将它们放置在 `tools/uvr5/uvr5_weights` 中。
中国地区用户可以进入以下链接并点击“下载副本”下载以上两个模型：
- [GPT-SoVITS Models](https://www.icloud.com.cn/iclouddrive/056y_Xog_HXpALuVUjscIwTtg#GPT-SoVITS_Models)
- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
对于中文自动语音识别（另外），从 [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), 和 [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) 下载模型，并将它们放置在 `tools/damo_asr/models` 中。
## 数据集格式
文本到语音（TTS）注释 .list 文件格式：
```
vocal_path|speaker_name|language|text
```
Language dictionary:
语言字典：
- 'zh': Chinese
- 'ja': Japanese
- 'en': English
Example:
示例：
```
D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
```
## Todo List
## 待办事项清单
- [ ] **High Priority:**
- [ ] **高优先级：**
- [x] Localization in Japanese and English.
- [ ] User guide.
- [x] Japanese and English dataset fine tune training.
- [x] 日语和英语的本地化。
- [ ] 用户指南。
- [x] 日语和英语数据集微调训练。
- [ ] **Features:**
- [ ] Zero-shot voice conversion (5s) / few-shot voice conversion (1min).
- [ ] TTS speaking speed control.
- [ ] Enhanced TTS emotion control.
- [ ] Experiment with changing SoVITS token inputs to probability distribution of vocabs.
- [ ] Improve English and Japanese text frontend.
- [ ] Develop tiny and larger-sized TTS models.
- [x] Colab scripts.
- [ ] Try expand training dataset (2k hours -> 10k hours).
- [ ] better sovits base model (enhanced audio quality)
- [ ] model mix
- [ ] 零样本声音转换（5 秒）/ 少样本声音转换（1 分钟）。
- [ ] TTS 语速控制。
- [ ] 增强的 TTS 情感控制。
- [ ] 尝试将 SoVITS 令牌输入更改为词汇的概率分布。
- [ ] 改进英语和日语文本前端。
- [ ] 开发体积小和更大的 TTS 模型。
- [x] Colab 脚本。
- [ ] 扩展训练数据集（从 2k 小时到 10k 小时）。
- [ ] 更好的 sovits 基础模型（增强的音频质量）。
- [ ] 模型混合。
## Credits
## 致谢
Special thanks to the following projects and contributors:
特别感谢以下项目和贡献者：
- [ar-vits](https://github.com/innnky/ar-vits)
- [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
@ -216,7 +214,7 @@ Special thanks to the following projects and contributors:
- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
- [gradio](https://github.com/gradio-app/gradio)
## Thanks to all contributors for their efforts
## 感谢所有贡献者的努力
<a href="https://github.com/RVC-Boss/GPT-SoVITS/graphs/contributors" target="_blank">
<img src="https://contrib.rocks/image?repo=RVC-Boss/GPT-SoVITS" />

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<div align="center">
<h1>GPT-SoVITS-WebUI</h1>
A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
[![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange)](https://github.com/RVC-Boss/GPT-SoVITS)
<img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br>
[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
[![Licence](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/🤗%20-Models%20Repo-yellow.svg?style=for-the-badge)](https://huggingface.co/lj1995/GPT-SoVITS/tree/main)
[**English**](./README.md) | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md)
</div>
---
> Check out our [demo video](https://www.bilibili.com/video/BV12g4y1m7Uw) here!
Unseen speakers few-shot fine-tuning demo:
https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-80c060ab47fb
For users in China region, you can use AutoDL Cloud Docker to experience the full functionality online: https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official
## Features:
1. **Zero-shot TTS:** Input a 5-second vocal sample and experience instant text-to-speech conversion.
2. **Few-shot TTS:** Fine-tune the model with just 1 minute of training data for improved voice similarity and realism.
3. **Cross-lingual Support:** Inference in languages different from the training dataset, currently supporting English, Japanese, and Chinese.
4. **WebUI Tools:** Integrated tools include voice accompaniment separation, automatic training set segmentation, Chinese ASR, and text labeling, assisting beginners in creating training datasets and GPT/SoVITS models.
## Environment Preparation
If you are a Windows user (tested with win>=10) you can install directly via the prezip. Just download the [prezip](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true), unzip it and double-click go-webui.bat to start GPT-SoVITS-WebUI.
### Tested Environments
- Python 3.9, PyTorch 2.0.1, CUDA 11
- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
- Python 3.9, PyTorch 2.3.0.dev20240122, macOS 14.3 (Apple silicon, GPU)
_Note: numba==0.56.4 require py<3.11_
### Quick Install with Conda
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
bash install.sh
```
### Install Manually
#### Pip Packages
```bash
pip install -r requirements.txt
```
#### FFmpeg
##### Conda Users
```bash
conda install ffmpeg
```
##### Ubuntu/Debian Users
```bash
sudo apt install ffmpeg
sudo apt install libsox-dev
conda install -c conda-forge 'ffmpeg<7'
```
##### MacOS Users
```bash
brew install ffmpeg
```
##### Windows Users
Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root.
### Pretrained Models
Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) and place them in `tools/uvr5/uvr5_weights`.
Users in China region can download these two models by entering the links below and clicking "Download a copy"
- [GPT-SoVITS Models](https://www.icloud.com.cn/iclouddrive/056y_Xog_HXpALuVUjscIwTtg#GPT-SoVITS_Models)
- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/damo_asr/models`.
### For Mac Users
If you are a Mac user, make sure you meet the following conditions for training and inferencing with GPU:
- Mac computers with Apple silicon or AMD GPUs
- macOS 12.3 or later
- Xcode command-line tools installed by running `xcode-select --install`
_Other Macs can do inference with CPU only._
Then install by using the following commands:
#### Create Environment
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
```
#### Install Requirements
```bash
pip install -r requirements.txt
pip uninstall torch torchaudio
pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
```
### Using Docker
#### docker-compose.yaml configuration
0. Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs.
1. Environment Variables：
- is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
2. Volumes Configuration，The application's root directory inside the container is set to /workspace. The default docker-compose.yaml lists some practical examples for uploading/downloading content.
3. shm_size： The default available memory for Docker Desktop on Windows is too small, which can cause abnormal operations. Adjust according to your own situation.
4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
#### Running with docker compose
```
docker compose -f "docker-compose.yaml" up -d
```
#### Running with docker command
As above, modify the corresponding parameters based on your actual situation, then run the following command:
```
docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9870:9870 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:xxxxx
```
## Dataset Format
The TTS annotation .list file format:
```
vocal_path|speaker_name|language|text
```
Language dictionary:
- 'zh': Chinese
- 'ja': Japanese
- 'en': English
Example:
```
D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
```
## Todo List
- [ ] **High Priority:**
- [x] Localization in Japanese and English.
- [ ] User guide.
- [x] Japanese and English dataset fine tune training.
- [ ] **Features:**
- [ ] Zero-shot voice conversion (5s) / few-shot voice conversion (1min).
- [ ] TTS speaking speed control.
- [ ] Enhanced TTS emotion control.
- [ ] Experiment with changing SoVITS token inputs to probability distribution of vocabs.
- [ ] Improve English and Japanese text frontend.
- [ ] Develop tiny and larger-sized TTS models.
- [x] Colab scripts.
- [ ] Try expand training dataset (2k hours -> 10k hours).
- [ ] better sovits base model (enhanced audio quality)
- [ ] model mix
## Credits
Special thanks to the following projects and contributors:
- [ar-vits](https://github.com/innnky/ar-vits)
- [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
- [vits](https://github.com/jaywalnut310/vits)
- [TransferTTS](https://github.com/hcy71o/TransferTTS/blob/master/models.py#L556)
- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
- [contentvec](https://github.com/auspicious3000/contentvec/)
- [hifi-gan](https://github.com/jik876/hifi-gan)
- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
- [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
- [audio-slicer](https://github.com/openvpi/audio-slicer)
- [SubFix](https://github.com/cronrpc/SubFix)
- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
- [gradio](https://github.com/gradio-app/gradio)
## Thanks to all contributors for their efforts
<a href="https://github.com/RVC-Boss/GPT-SoVITS/graphs/contributors" target="_blank">
<img src="https://contrib.rocks/image?repo=RVC-Boss/GPT-SoVITS" />
</a>