From fb257f32e693f3a68608ff882183f1826830842e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=99=86=E5=B2=A9=E6=B7=85?= <131521928+Micro-ATP@users.noreply.github.com> Date: Fri, 2 Feb 2024 14:23:44 +0800 Subject: [PATCH] update --- README.md | 230 +++++++++++++++++++++++----------------------- docs/en/README.md | 223 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 337 insertions(+), 116 deletions(-) create mode 100644 docs/en/README.md diff --git a/README.md b/README.md index 958e314f..da3051c9 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@

GPT-SoVITS-WebUI

-A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.

+强大的少样本语音转换与语音合成Web用户界面。

[![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.

[![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)
--- -> 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 diff --git a/docs/en/README.md b/docs/en/README.md new file mode 100644 index 00000000..958e314f --- /dev/null +++ b/docs/en/README.md @@ -0,0 +1,223 @@ +
+ +

GPT-SoVITS-WebUI

+A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.

+ +[![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) + +
+ +[![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) + +
+ +--- + +> 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 + +
+ +