From 7bc52a58158f53779c86b238cafc92c1441f6f53 Mon Sep 17 00:00:00 2001 From: fanpt <320622572@qq.com> Date: Mon, 4 Aug 2025 14:12:28 +0800 Subject: [PATCH] update README.md --- README.md | 484 +++++--------------------------------------------- README_bac.md | 478 +++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 526 insertions(+), 436 deletions(-) create mode 100644 README_bac.md diff --git a/README.md b/README.md index 86d50ea..b3804ec 100644 --- a/README.md +++ b/README.md @@ -1,478 +1,90 @@ -
+# GPT_SoVITS 服务 README -

GPT-SoVITS-WebUI

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

+## 简要说明 -[![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) +克隆音色并提供实时 TTS Web API，可动态切换 GPT/Sovits 权重，支持流式输出。 -RVC-Boss%2FGPT-SoVITS | Trendshift +## 启动 - - -[![Python](https://img.shields.io/badge/python-3.10--3.12-blue?style=for-the-badge&logo=python)](https://www.python.org) -[![GitHub release](https://img.shields.io/github/v/release/RVC-Boss/gpt-sovits?style=for-the-badge&logo=github)](https://github.com/RVC-Boss/gpt-sovits/releases) - -[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb) -[![Huggingface](https://img.shields.io/badge/免费在线体验-free_online_demo-yellow.svg?style=for-the-badge&logo=huggingface)](https://lj1995-gpt-sovits-proplus.hf.space/) -[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits) - -[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) -[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/) -[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/en/Changelog_EN.md) -[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE) - -**English** | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md) | [**Türkçe**](./docs/tr/README.md) - -
- ---- - -## 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, Korean, Cantonese 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. - -**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 - -**RTF(inference speed) of GPT-SoVITS v2 ProPlus**: -0.028 tested in 4060Ti, 0.014 tested in 4090 (1400words~=4min, inference time is 3.36s), 0.526 in M4 CPU. You can test our [huggingface demo](https://lj1995-gpt-sovits-proplus.hf.space/) (half H200) to experience high-speed inference . - -请不要尬黑GPT-SoVITS推理速度慢，谢谢！ - -**User guide: [简体中文](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) | [English](https://rentry.co/GPT-SoVITS-guide#/)** - -## Installation - -For users in China, you can [click here](https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official) to use AutoDL Cloud Docker to experience the full functionality online. - -### Tested Environments - -| Python Version | PyTorch Version | Device | -| -------------- | ---------------- | ------------- | -| Python 3.10 | PyTorch 2.5.1 | CUDA 12.4 | -| Python 3.11 | PyTorch 2.5.1 | CUDA 12.4 | -| Python 3.11 | PyTorch 2.7.0 | CUDA 12.8 | -| Python 3.9 | PyTorch 2.8.0dev | CUDA 12.8 | -| Python 3.9 | PyTorch 2.5.1 | Apple silicon | -| Python 3.11 | PyTorch 2.7.0 | Apple silicon | -| Python 3.9 | PyTorch 2.2.2 | CPU | - -### Windows - -If you are a Windows user (tested with win>=10), you can [download the integrated package](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true) and double-click on _go-webui.bat_ to start GPT-SoVITS-WebUI. - -**Users in China can [download the package here](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#KTvnO).** - -Install the program by running the following commands: - -```pwsh -conda create -n GPTSoVits python=3.10 -conda activate GPTSoVits -pwsh -F install.ps1 --Device --Source [--DownloadUVR5] ``` +python api_v2.py -a 0.0.0.0 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml +```` -### Linux - -```bash -conda create -n GPTSoVits python=3.10 -conda activate GPTSoVits -bash install.sh --device --source [--download-uvr5] -``` - -### macOS - -**Note: The models trained with GPUs on Macs result in significantly lower quality compared to those trained on other devices, so we are temporarily using CPUs instead.** +## 后台运行与日志 -Install the program by running the following commands: - -```bash -conda create -n GPTSoVits python=3.10 -conda activate GPTSoVits -bash install.sh --device --source [--download-uvr5] ``` - -### Install Manually - -#### Install Dependences - -```bash -conda create -n GPTSoVits python=3.10 -conda activate GPTSoVits - -pip install -r extra-req.txt --no-deps -pip install -r requirements.txt +nohup python api_v2.py -a 0.0.0.0 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml > log/gptsovits.log 2>&1 & +tail -n 50 -f log/gptsovits.log ``` -#### Install FFmpeg +## 主要接口 -##### Conda Users +### 1. /tts -```bash -conda activate GPTSoVits -conda install ffmpeg -``` +合成语音（GET/POST），成功返回 WAV 流，失败返回 JSON 错误。 -##### Ubuntu/Debian Users +**必需字段（POST 示例）：** -```bash -sudo apt install ffmpeg -sudo apt install libsox-dev +```json +{ + "text": "要合成的文本", + "text_lang": "zh", + "ref_audio_path": "参考音频.wav", + "prompt_lang": "zh" +} ``` -##### Windows Users +可选字段有 `prompt_text`、`aux_ref_audio_paths`、`streaming_mode`、`speed_factor` 等。 -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 +### 2. /control -Install [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) +控制服务。`command` 可为 `restart` 或 `exit`。 -##### MacOS Users +GET 示例： -```bash -brew install ffmpeg ``` - -### Running GPT-SoVITS with Docker - -#### Docker Image Selection - -Due to rapid development in the codebase and a slower Docker image release cycle, please: - -- Check [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) for the latest available image tags -- Choose an appropriate image tag for your environment -- `Lite` means the Docker image **does not include** ASR models and UVR5 models. You can manually download the UVR5 models, while the program will automatically download the ASR models as needed -- The appropriate architecture image (amd64/arm64) will be automatically pulled during Docker Compose -- Docker Compose will mount **all files** in the current directory. Please switch to the project root directory and **pull the latest code** before using the Docker image -- Optionally, build the image locally using the provided Dockerfile for the most up-to-date changes - -#### Environment Variables - -- `is_half`: Controls whether half-precision (fp16) is enabled. Set to `true` if your GPU supports it to reduce memory usage. - -#### Shared Memory Configuration - -On Windows (Docker Desktop), the default shared memory size is small and may cause unexpected behavior. Increase `shm_size` (e.g., to `16g`) in your Docker Compose file based on your available system memory. - -#### Choosing a Service - -The `docker-compose.yaml` defines two services: - -- `GPT-SoVITS-CU126` & `GPT-SoVITS-CU128`: Full version with all features. -- `GPT-SoVITS-CU126-Lite` & `GPT-SoVITS-CU128-Lite`: Lightweight version with reduced dependencies and functionality. - -To run a specific service with Docker Compose, use: - -```bash -docker compose run --service-ports +/control?command=restart ``` -#### Building the Docker Image Locally - -If you want to build the image yourself, use: +POST 示例： -```bash -bash docker_build.sh --cuda <12.6|12.8> [--lite] +```json +{ "command": "restart" } ``` -#### Accessing the Running Container (Bash Shell) +### 3. /set\_gpt\_weights -Once the container is running in the background, you can access it using: +切换 GPT 模型权重： -```bash -docker exec -it bash ``` - -## Pretrained Models - -**If `install.sh` runs successfully, you may skip No.1,2,3** - -**Users in China can [download all these models here](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#nVNhX).** - -1. Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`. - -2. Download G2PW models from [G2PWModel.zip(HF)](https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip)| [G2PWModel.zip(ModelScope)](https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip), unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.(Chinese TTS Only) - -3. 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`. - - - If you want to use `bs_roformer` or `mel_band_roformer` models for UVR5, you can manually download the model and corresponding configuration file, and put them in `tools/uvr5/uvr5_weights`. **Rename the model file and configuration file, ensure that the model and configuration files have the same and corresponding names except for the suffix**. In addition, the model and configuration file names **must include `roformer`** in order to be recognized as models of the roformer class. - - - The suggestion is to **directly specify the model type** in the model name and configuration file name, such as `mel_mand_roformer`, `bs_roformer`. If not specified, the features will be compared from the configuration file to determine which type of model it is. For example, the model `bs_roformer_ep_368_sdr_12.9628.ckpt` and its corresponding configuration file `bs_roformer_ep_368_sdr_12.9628.yaml` are a pair, `kim_mel_band_roformer.ckpt` and `kim_mel_band_roformer.yaml` are also a pair. - -4. 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/asr/models`. - -5. For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint. - -## Dataset Format - -The TTS annotation .list file format: - -``` - -vocal_path|speaker_name|language|text - +/set_gpt_weights?weights_path=路径.ckpt ``` -Language dictionary: +返回 `"success"` 或错误 JSON。 -- 'zh': Chinese -- 'ja': Japanese -- 'en': English -- 'ko': Korean -- 'yue': Cantonese +### 4. /set\_sovits\_weights -Example: +切换 Sovits 模型权重： ``` - -D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin. - +/set_sovits_weights?weights_path=路径.pth ``` -## Finetune and inference - -### Open WebUI - -#### Integrated Package Users +返回 `"success"` 或错误 JSON。 -Double-click `go-webui.bat`or use `go-webui.ps1` -if you want to switch to V1,then double-click`go-webui-v1.bat` or use `go-webui-v1.ps1` +## 示例 curl -#### Others - -```bash -python webui.py ``` - -if you want to switch to V1,then - -```bash -python webui.py v1 +curl --location --request POST 'http://192.168.10.113:9880/tts' \ +--header 'Content-Type: application/json' \ +--data-raw '{ + "text": "先帝创业未半而中道崩殂。", + "text_lang": "zh", + "ref_audio_path": "input/gentle_girl.wav", + "prompt_lang": "zh", + "prompt_text": "刚进直播间的宝子们，左上角先点个关注，点亮咱们家的粉丝灯牌！我是你们的主播陈婉婉，今天给大家准备了超级重磅的福利", + "media_type": "wav", + "streaming_mode": false +}' ``` -Or maunally switch version in WebUI - -### Finetune - -#### Path Auto-filling is now supported - -1. Fill in the audio path -2. Slice the audio into small chunks -3. Denoise(optinal) -4. ASR -5. Proofreading ASR transcriptions -6. Go to the next Tab, then finetune the model - -### Open Inference WebUI - -#### Integrated Package Users - -Double-click `go-webui-v2.bat` or use `go-webui-v2.ps1` ,then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference` - -#### Others - -```bash -python GPT_SoVITS/inference_webui.py -``` - -OR - -```bash -python webui.py -``` - -then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference` - -## V2 Release Notes - -New Features: - -1. Support Korean and Cantonese - -2. An optimized text frontend - -3. Pre-trained model extended from 2k hours to 5k hours - -4. Improved synthesis quality for low-quality reference audio - - [more details]() - -Use v2 from v1 environment: - -1. `pip install -r requirements.txt` to update some packages - -2. Clone the latest codes from github. - -3. Download v2 pretrained models from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) and put them into `GPT_SoVITS/pretrained_models/gsv-v2final-pretrained`. - - Chinese v2 additional: [G2PWModel.zip(HF)](https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip)| [G2PWModel.zip(ModelScope)](https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip)(Download G2PW models, unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.) - -## V3 Release Notes - -New Features: - -1. The timbre similarity is higher, requiring less training data to approximate the target speaker (the timbre similarity is significantly improved using the base model directly without fine-tuning). - -2. GPT model is more stable, with fewer repetitions and omissions, and it is easier to generate speech with richer emotional expression. - - [more details]() - -Use v3 from v2 environment: - -1. `pip install -r requirements.txt` to update some packages - -2. Clone the latest codes from github. - -3. Download v3 pretrained models (s1v3.ckpt, s2Gv3.pth and models--nvidia--bigvgan_v2_24khz_100band_256x folder) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`. - - additional: for Audio Super Resolution model, you can read [how to download](./tools/AP_BWE_main/24kto48k/readme.txt) - -## V4 Release Notes - -New Features: - -1. Version 4 fixes the issue of metallic artifacts in Version 3 caused by non-integer multiple upsampling, and natively outputs 48k audio to prevent muffled sound (whereas Version 3 only natively outputs 24k audio). The author considers Version 4 a direct replacement for Version 3, though further testing is still needed. - [more details]() - -Use v4 from v1/v2/v3 environment: - -1. `pip install -r requirements.txt` to update some packages - -2. Clone the latest codes from github. - -3. Download v4 pretrained models (gsv-v4-pretrained/s2v4.ckpt, and gsv-v4-pretrained/vocoder.pth) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`. - -## V2Pro Release Notes - -New Features: - -1. Slightly higher VRAM usage than v2, surpassing v4's performance, with v2's hardware cost and speed. - [more details]() - -2.v1/v2 and the v2Pro series share the same characteristics, while v3/v4 have similar features. For training sets with average audio quality, v1/v2/v2Pro can deliver decent results, but v3/v4 cannot. Additionally, the synthesized tone and timebre of v3/v4 lean more toward the reference audio rather than the overall training set. - -Use v2Pro from v1/v2/v3/v4 environment: - -1. `pip install -r requirements.txt` to update some packages - -2. Clone the latest codes from github. - -3. Download v2Pro pretrained models (v2Pro/s2Dv2Pro.pth, v2Pro/s2Gv2Pro.pth, v2Pro/s2Dv2ProPlus.pth, v2Pro/s2Gv2ProPlus.pth, and sv/pretrained_eres2netv2w24s4ep4.ckpt) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`. - -## Todo List - -- [x] **High Priority:** - - - [x] Localization in Japanese and English. - - [x] User guide. - - [x] Japanese and English dataset fine tune training. - -- [ ] **Features:** - - [x] Zero-shot voice conversion (5s) / few-shot voice conversion (1min). - - [x] TTS speaking speed control. - - [ ] ~~Enhanced TTS emotion control.~~ Maybe use pretrained finetuned preset GPT models for better emotion. - - [ ] Experiment with changing SoVITS token inputs to probability distribution of GPT vocabs (transformer latent). - - [x] Improve English and Japanese text frontend. - - [ ] Develop tiny and larger-sized TTS models. - - [x] Colab scripts. - - [x] Try expand training dataset (2k hours -> 10k hours). - - [x] better sovits base model (enhanced audio quality) - - [ ] model mix - -## (Additional) Method for running from the command line - -Use the command line to open the WebUI for UVR5 - -```bash -python tools/uvr5/webui.py "" -``` - - - -This is how the audio segmentation of the dataset is done using the command line - -```bash -python audio_slicer.py \ - --input_path "" \ - --output_root "" \ - --threshold \ - --min_length \ - --min_interval - --hop_size -``` - -This is how dataset ASR processing is done using the command line(Only Chinese) - -```bash -python tools/asr/funasr_asr.py -i -o -``` - -ASR processing is performed through Faster_Whisper(ASR marking except Chinese) - -(No progress bars, GPU performance may cause time delays) - -```bash -python ./tools/asr/fasterwhisper_asr.py -i -o -l -p -``` - -A custom list save path is enabled - -## Credits - -Special thanks to the following projects and contributors: - -### Theoretical Research - -- [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) -- [contentvec](https://github.com/auspicious3000/contentvec/) -- [hifi-gan](https://github.com/jik876/hifi-gan) -- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41) -- [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py) -- [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py) - -### Pretrained Models - -- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain) -- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) -- [BigVGAN](https://github.com/NVIDIA/BigVGAN) -- [eresnetv2](https://modelscope.cn/models/iic/speech_eres2netv2w24s4ep4_sv_zh-cn_16k-common) - -### Text Frontend for Inference - -- [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization) -- [split-lang](https://github.com/DoodleBears/split-lang) -- [g2pW](https://github.com/GitYCC/g2pW) -- [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW) -- [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw) - -### WebUI Tools - -- [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) -- [faster-whisper](https://github.com/SYSTRAN/faster-whisper) -- [FunASR](https://github.com/alibaba-damo-academy/FunASR) -- [AP-BWE](https://github.com/yxlu-0102/AP-BWE) - -Thankful to @Naozumi520 for providing the Cantonese training set and for the guidance on Cantonese-related knowledge. - -## Thanks to all contributors for their efforts - - - - diff --git a/README_bac.md b/README_bac.md new file mode 100644 index 0000000..86d50ea --- /dev/null +++ b/README_bac.md @@ -0,0 +1,478 @@ +
+ +

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) + +RVC-Boss%2FGPT-SoVITS | Trendshift + + + +[![Python](https://img.shields.io/badge/python-3.10--3.12-blue?style=for-the-badge&logo=python)](https://www.python.org) +[![GitHub release](https://img.shields.io/github/v/release/RVC-Boss/gpt-sovits?style=for-the-badge&logo=github)](https://github.com/RVC-Boss/gpt-sovits/releases) + +[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb) +[![Huggingface](https://img.shields.io/badge/免费在线体验-free_online_demo-yellow.svg?style=for-the-badge&logo=huggingface)](https://lj1995-gpt-sovits-proplus.hf.space/) +[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits) + +[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) +[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/) +[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/en/Changelog_EN.md) +[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE) + +**English** | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md) | [**Türkçe**](./docs/tr/README.md) + +
+ +--- + +## 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, Korean, Cantonese 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. + +**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 + +**RTF(inference speed) of GPT-SoVITS v2 ProPlus**: +0.028 tested in 4060Ti, 0.014 tested in 4090 (1400words~=4min, inference time is 3.36s), 0.526 in M4 CPU. You can test our [huggingface demo](https://lj1995-gpt-sovits-proplus.hf.space/) (half H200) to experience high-speed inference . + +请不要尬黑GPT-SoVITS推理速度慢，谢谢！ + +**User guide: [简体中文](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) | [English](https://rentry.co/GPT-SoVITS-guide#/)** + +## Installation + +For users in China, you can [click here](https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official) to use AutoDL Cloud Docker to experience the full functionality online. + +### Tested Environments + +| Python Version | PyTorch Version | Device | +| -------------- | ---------------- | ------------- | +| Python 3.10 | PyTorch 2.5.1 | CUDA 12.4 | +| Python 3.11 | PyTorch 2.5.1 | CUDA 12.4 | +| Python 3.11 | PyTorch 2.7.0 | CUDA 12.8 | +| Python 3.9 | PyTorch 2.8.0dev | CUDA 12.8 | +| Python 3.9 | PyTorch 2.5.1 | Apple silicon | +| Python 3.11 | PyTorch 2.7.0 | Apple silicon | +| Python 3.9 | PyTorch 2.2.2 | CPU | + +### Windows + +If you are a Windows user (tested with win>=10), you can [download the integrated package](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true) and double-click on _go-webui.bat_ to start GPT-SoVITS-WebUI. + +**Users in China can [download the package here](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#KTvnO).** + +Install the program by running the following commands: + +```pwsh +conda create -n GPTSoVits python=3.10 +conda activate GPTSoVits +pwsh -F install.ps1 --Device --Source [--DownloadUVR5] +``` + +### Linux + +```bash +conda create -n GPTSoVits python=3.10 +conda activate GPTSoVits +bash install.sh --device --source [--download-uvr5] +``` + +### macOS + +**Note: The models trained with GPUs on Macs result in significantly lower quality compared to those trained on other devices, so we are temporarily using CPUs instead.** + +Install the program by running the following commands: + +```bash +conda create -n GPTSoVits python=3.10 +conda activate GPTSoVits +bash install.sh --device --source [--download-uvr5] +``` + +### Install Manually + +#### Install Dependences + +```bash +conda create -n GPTSoVits python=3.10 +conda activate GPTSoVits + +pip install -r extra-req.txt --no-deps +pip install -r requirements.txt +``` + +#### Install FFmpeg + +##### Conda Users + +```bash +conda activate GPTSoVits +conda install ffmpeg +``` + +##### Ubuntu/Debian Users + +```bash +sudo apt install ffmpeg +sudo apt install libsox-dev +``` + +##### 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 + +Install [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) + +##### MacOS Users + +```bash +brew install ffmpeg +``` + +### Running GPT-SoVITS with Docker + +#### Docker Image Selection + +Due to rapid development in the codebase and a slower Docker image release cycle, please: + +- Check [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) for the latest available image tags +- Choose an appropriate image tag for your environment +- `Lite` means the Docker image **does not include** ASR models and UVR5 models. You can manually download the UVR5 models, while the program will automatically download the ASR models as needed +- The appropriate architecture image (amd64/arm64) will be automatically pulled during Docker Compose +- Docker Compose will mount **all files** in the current directory. Please switch to the project root directory and **pull the latest code** before using the Docker image +- Optionally, build the image locally using the provided Dockerfile for the most up-to-date changes + +#### Environment Variables + +- `is_half`: Controls whether half-precision (fp16) is enabled. Set to `true` if your GPU supports it to reduce memory usage. + +#### Shared Memory Configuration + +On Windows (Docker Desktop), the default shared memory size is small and may cause unexpected behavior. Increase `shm_size` (e.g., to `16g`) in your Docker Compose file based on your available system memory. + +#### Choosing a Service + +The `docker-compose.yaml` defines two services: + +- `GPT-SoVITS-CU126` & `GPT-SoVITS-CU128`: Full version with all features. +- `GPT-SoVITS-CU126-Lite` & `GPT-SoVITS-CU128-Lite`: Lightweight version with reduced dependencies and functionality. + +To run a specific service with Docker Compose, use: + +```bash +docker compose run --service-ports +``` + +#### Building the Docker Image Locally + +If you want to build the image yourself, use: + +```bash +bash docker_build.sh --cuda <12.6|12.8> [--lite] +``` + +#### Accessing the Running Container (Bash Shell) + +Once the container is running in the background, you can access it using: + +```bash +docker exec -it bash +``` + +## Pretrained Models + +**If `install.sh` runs successfully, you may skip No.1,2,3** + +**Users in China can [download all these models here](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#nVNhX).** + +1. Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`. + +2. Download G2PW models from [G2PWModel.zip(HF)](https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip)| [G2PWModel.zip(ModelScope)](https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip), unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.(Chinese TTS Only) + +3. 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`. + + - If you want to use `bs_roformer` or `mel_band_roformer` models for UVR5, you can manually download the model and corresponding configuration file, and put them in `tools/uvr5/uvr5_weights`. **Rename the model file and configuration file, ensure that the model and configuration files have the same and corresponding names except for the suffix**. In addition, the model and configuration file names **must include `roformer`** in order to be recognized as models of the roformer class. + + - The suggestion is to **directly specify the model type** in the model name and configuration file name, such as `mel_mand_roformer`, `bs_roformer`. If not specified, the features will be compared from the configuration file to determine which type of model it is. For example, the model `bs_roformer_ep_368_sdr_12.9628.ckpt` and its corresponding configuration file `bs_roformer_ep_368_sdr_12.9628.yaml` are a pair, `kim_mel_band_roformer.ckpt` and `kim_mel_band_roformer.yaml` are also a pair. + +4. 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/asr/models`. + +5. For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint. + +## Dataset Format + +The TTS annotation .list file format: + +``` + +vocal_path|speaker_name|language|text + +``` + +Language dictionary: + +- 'zh': Chinese +- 'ja': Japanese +- 'en': English +- 'ko': Korean +- 'yue': Cantonese + +Example: + +``` + +D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin. + +``` + +## Finetune and inference + +### Open WebUI + +#### Integrated Package Users + +Double-click `go-webui.bat`or use `go-webui.ps1` +if you want to switch to V1,then double-click`go-webui-v1.bat` or use `go-webui-v1.ps1` + +#### Others + +```bash +python webui.py +``` + +if you want to switch to V1,then + +```bash +python webui.py v1 +``` + +Or maunally switch version in WebUI + +### Finetune + +#### Path Auto-filling is now supported + +1. Fill in the audio path +2. Slice the audio into small chunks +3. Denoise(optinal) +4. ASR +5. Proofreading ASR transcriptions +6. Go to the next Tab, then finetune the model + +### Open Inference WebUI + +#### Integrated Package Users + +Double-click `go-webui-v2.bat` or use `go-webui-v2.ps1` ,then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference` + +#### Others + +```bash +python GPT_SoVITS/inference_webui.py +``` + +OR + +```bash +python webui.py +``` + +then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference` + +## V2 Release Notes + +New Features: + +1. Support Korean and Cantonese + +2. An optimized text frontend + +3. Pre-trained model extended from 2k hours to 5k hours + +4. Improved synthesis quality for low-quality reference audio + + [more details]() + +Use v2 from v1 environment: + +1. `pip install -r requirements.txt` to update some packages + +2. Clone the latest codes from github. + +3. Download v2 pretrained models from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) and put them into `GPT_SoVITS/pretrained_models/gsv-v2final-pretrained`. + + Chinese v2 additional: [G2PWModel.zip(HF)](https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip)| [G2PWModel.zip(ModelScope)](https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip)(Download G2PW models, unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.) + +## V3 Release Notes + +New Features: + +1. The timbre similarity is higher, requiring less training data to approximate the target speaker (the timbre similarity is significantly improved using the base model directly without fine-tuning). + +2. GPT model is more stable, with fewer repetitions and omissions, and it is easier to generate speech with richer emotional expression. + + [more details]() + +Use v3 from v2 environment: + +1. `pip install -r requirements.txt` to update some packages + +2. Clone the latest codes from github. + +3. Download v3 pretrained models (s1v3.ckpt, s2Gv3.pth and models--nvidia--bigvgan_v2_24khz_100band_256x folder) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`. + + additional: for Audio Super Resolution model, you can read [how to download](./tools/AP_BWE_main/24kto48k/readme.txt) + +## V4 Release Notes + +New Features: + +1. Version 4 fixes the issue of metallic artifacts in Version 3 caused by non-integer multiple upsampling, and natively outputs 48k audio to prevent muffled sound (whereas Version 3 only natively outputs 24k audio). The author considers Version 4 a direct replacement for Version 3, though further testing is still needed. + [more details]() + +Use v4 from v1/v2/v3 environment: + +1. `pip install -r requirements.txt` to update some packages + +2. Clone the latest codes from github. + +3. Download v4 pretrained models (gsv-v4-pretrained/s2v4.ckpt, and gsv-v4-pretrained/vocoder.pth) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`. + +## V2Pro Release Notes + +New Features: + +1. Slightly higher VRAM usage than v2, surpassing v4's performance, with v2's hardware cost and speed. + [more details]() + +2.v1/v2 and the v2Pro series share the same characteristics, while v3/v4 have similar features. For training sets with average audio quality, v1/v2/v2Pro can deliver decent results, but v3/v4 cannot. Additionally, the synthesized tone and timebre of v3/v4 lean more toward the reference audio rather than the overall training set. + +Use v2Pro from v1/v2/v3/v4 environment: + +1. `pip install -r requirements.txt` to update some packages + +2. Clone the latest codes from github. + +3. Download v2Pro pretrained models (v2Pro/s2Dv2Pro.pth, v2Pro/s2Gv2Pro.pth, v2Pro/s2Dv2ProPlus.pth, v2Pro/s2Gv2ProPlus.pth, and sv/pretrained_eres2netv2w24s4ep4.ckpt) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`. + +## Todo List + +- [x] **High Priority:** + + - [x] Localization in Japanese and English. + - [x] User guide. + - [x] Japanese and English dataset fine tune training. + +- [ ] **Features:** + - [x] Zero-shot voice conversion (5s) / few-shot voice conversion (1min). + - [x] TTS speaking speed control. + - [ ] ~~Enhanced TTS emotion control.~~ Maybe use pretrained finetuned preset GPT models for better emotion. + - [ ] Experiment with changing SoVITS token inputs to probability distribution of GPT vocabs (transformer latent). + - [x] Improve English and Japanese text frontend. + - [ ] Develop tiny and larger-sized TTS models. + - [x] Colab scripts. + - [x] Try expand training dataset (2k hours -> 10k hours). + - [x] better sovits base model (enhanced audio quality) + - [ ] model mix + +## (Additional) Method for running from the command line + +Use the command line to open the WebUI for UVR5 + +```bash +python tools/uvr5/webui.py "" +``` + + + +This is how the audio segmentation of the dataset is done using the command line + +```bash +python audio_slicer.py \ + --input_path "" \ + --output_root "" \ + --threshold \ + --min_length \ + --min_interval + --hop_size +``` + +This is how dataset ASR processing is done using the command line(Only Chinese) + +```bash +python tools/asr/funasr_asr.py -i -o +``` + +ASR processing is performed through Faster_Whisper(ASR marking except Chinese) + +(No progress bars, GPU performance may cause time delays) + +```bash +python ./tools/asr/fasterwhisper_asr.py -i -o -l -p +``` + +A custom list save path is enabled + +## Credits + +Special thanks to the following projects and contributors: + +### Theoretical Research + +- [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) +- [contentvec](https://github.com/auspicious3000/contentvec/) +- [hifi-gan](https://github.com/jik876/hifi-gan) +- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41) +- [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py) +- [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py) + +### Pretrained Models + +- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain) +- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) +- [BigVGAN](https://github.com/NVIDIA/BigVGAN) +- [eresnetv2](https://modelscope.cn/models/iic/speech_eres2netv2w24s4ep4_sv_zh-cn_16k-common) + +### Text Frontend for Inference + +- [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization) +- [split-lang](https://github.com/DoodleBears/split-lang) +- [g2pW](https://github.com/GitYCC/g2pW) +- [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW) +- [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw) + +### WebUI Tools + +- [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) +- [faster-whisper](https://github.com/SYSTRAN/faster-whisper) +- [FunASR](https://github.com/alibaba-damo-academy/FunASR) +- [AP-BWE](https://github.com/yxlu-0102/AP-BWE) + +Thankful to @Naozumi520 for providing the Cantonese training set and for the guidance on Cantonese-related knowledge. + +## Thanks to all contributors for their efforts + + + +