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[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
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[![Huggingface](https://img.shields.io/badge/🤗%20-Models%20Repo-yellow.svg?style=for-the-badge)](https://huggingface.co/lj1995/GPT-SoVITS/tree/main)
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[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
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**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)
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</div>
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---
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1. **Zero-shot TTS:** Input a 5-second vocal sample and experience instant text-to-speech conversion.
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2. **Few-shot TTS:** Fine-tune the model with just 1 minute of training data for improved voice similarity and realism.
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3. **Cross-lingual Support:** Inference in languages different from the training dataset, currently supporting English, Japanese, and Chinese.
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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.
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**Check out our [demo video](https://www.bilibili.com/video/BV12g4y1m7Uw) here!**
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Unseen speakers few-shot fine-tuning demo:
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https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-80c060ab47fb
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**User guide: [简体中文](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) | [English](https://rentry.co/GPT-SoVITS-guide#/)**
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## Installation
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For users in the China region, 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.
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### Tested Environments
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- Python 3.9, PyTorch 2.0.1, CUDA 11
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- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
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- Python 3.9, PyTorch 2.2.2, macOS 14.4.1 (Apple silicon)
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- Python 3.9, PyTorch 2.2.2, CPU devices
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_Note: numba==0.56.4 requires py<3.11_
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### Windows
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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-beta.7z?download=true) and double-click on _go-webui.bat_ to start GPT-SoVITS-WebUI.
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Users in the China region can [download the package](https://www.icloud.com.cn/iclouddrive/030K8WjGJ9xMXhpzJVIMEWPzQ#GPT-SoVITS-beta0706fix1) by clicking the link and then selecting "Download a copy." (Log out if you encounter errors while downloading.)
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### Linux
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```bash
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conda create -n GPTSoVits python=3.9
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conda activate GPTSoVits
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bash install.sh
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```
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### macOS
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**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.**
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1. Install Xcode command-line tools by running `xcode-select --install`.
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2. Install FFmpeg by running `brew install ffmpeg`.
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3. Install the program by running the following commands:
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```bash
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conda create -n GPTSoVits python=3.9
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conda activate GPTSoVits
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pip install -r requirements.txt
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```
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### Install Manually
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#### Install FFmpeg
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##### Conda Users
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```bash
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conda install ffmpeg
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```
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##### Ubuntu/Debian Users
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```bash
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sudo apt install ffmpeg
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sudo apt install libsox-dev
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conda install -c conda-forge 'ffmpeg<7'
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```
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##### Windows Users
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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.
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Install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) (Korean TTS Only)
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##### MacOS Users
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```bash
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brew install ffmpeg
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```
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#### Install Dependences
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```bash
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pip install -r requirements.txt
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```
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### Using Docker
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#### docker-compose.yaml configuration
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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.
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1. Environment Variables:
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- 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.
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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.
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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.
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4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
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#### Running with docker compose
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```
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docker compose -f "docker-compose.yaml" up -d
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```
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#### Running with docker command
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As above, modify the corresponding parameters based on your actual situation, then run the following command:
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```
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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 9880:9880 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:xxxxx
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```
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## Pretrained Models
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Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
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Download G2PW models from [G2PWModel-v2-onnx.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip), unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS\text`.(Chinese TTS Only)
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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`.
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Users in the China region can download these two models by entering the links below and clicking "Download a copy" (Log out if you encounter errors while downloading.)
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- [GPT-SoVITS Models](https://www.icloud.com/iclouddrive/044boFMiOHHt22SNr-c-tirbA#pretrained_models)
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- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
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- [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)(Download G2PW models, unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS\text`.
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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`.
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Or Download FunASR Model from [FunASR Model](https://www.icloud.com/iclouddrive/0b52_7SQWYr75kHkPoPXgpeQA#models), unzip and replace `tools/asr/models`.(Log out if you encounter errors while downloading.)
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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.
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Users in the China region can download this model by entering the links below
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- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/00bUEp9_mcjMq_dhHu_vrAFDQ#faster-whisper-large-v3) (Click "Download a copy", log out if you encounter errors while downloading.)
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- [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3) (HuggingFace mirror site)
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## Dataset Format
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The TTS annotation .list file format:
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```
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vocal_path|speaker_name|language|text
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```
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Language dictionary:
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- 'zh': Chinese
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- 'ja': Japanese
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- 'en': English
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- 'ko': Korean
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- 'yue': Cantonese
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Example:
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```
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D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
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```
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## Finetune and inference
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### Open WebUI
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#### Integrated Package Users
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Double-click `go-webui.bat`or use `go-webui.ps`
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if you want to switch to V1,then double-click`go-webui-v1.bat` or use `go-webui-v1.ps`
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#### Others
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```bash
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python webui.py <language(optional)>
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```
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if you want to switch to V1,then
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```bash
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python webui.py v1 <language(optional)>
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```
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Or maunally switch version in WebUI
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### Finetune
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#### Path Auto-filling is now supported
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1.Fill in the audio path
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2.Slice the audio into small chunks
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3.Denoise(optinal)
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4.ASR
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5.Proofreading ASR transcriptions
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6.Go to the next Tab, then finetune the model
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### Open Inference WebUI
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#### Integrated Package Users
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Double-click `go-webui-v2.bat` or use `go-webui-v2.ps` ,then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
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#### Others
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```bash
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python GPT_SoVITS/inference_webui.py <language(optional)>
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```
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OR
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```bash
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python webui.py
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```
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then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
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## V2 Release Notes
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New Features:
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1.Support Korean and Cantonese
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2.An optimized text frontend
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3.Pre-trained model extended from 2k hours to 5k hours
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4.Improved synthesis quality for low-quality reference audio
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[more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7) )
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Use v2 from v1 environment:
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1.pip install -r requirements.txt to update some packages
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2.clone the latest codes from github
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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
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Chinese v2 additional: [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)(Download G2PW models, unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS\text`.
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## Todo List
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- [x] **High Priority:**
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- [x] Localization in Japanese and English.
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- [x] User guide.
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- [x] Japanese and English dataset fine tune training.
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- [ ] **Features:**
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- [x] Zero-shot voice conversion (5s) / few-shot voice conversion (1min).
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- [x] TTS speaking speed control.
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- [ ] ~~Enhanced TTS emotion control.~~
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- [ ] Experiment with changing SoVITS token inputs to probability distribution of GPT vocabs (transformer latent).
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- [x] Improve English and Japanese text frontend.
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- [ ] Develop tiny and larger-sized TTS models.
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- [x] Colab scripts.
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- [ ] Try expand training dataset (2k hours -> 10k hours).
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- [x] better sovits base model (enhanced audio quality)
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- [ ] model mix
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## (Additional) Method for running from the command line
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Use the command line to open the WebUI for UVR5
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```
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python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
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```
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<!-- If you can't open a browser, follow the format below for UVR processing,This is using mdxnet for audio processing
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```
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python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
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``` -->
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This is how the audio segmentation of the dataset is done using the command line
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```
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python audio_slicer.py \
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--input_path "<path_to_original_audio_file_or_directory>" \
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--output_root "<directory_where_subdivided_audio_clips_will_be_saved>" \
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--threshold <volume_threshold> \
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--min_length <minimum_duration_of_each_subclip> \
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--min_interval <shortest_time_gap_between_adjacent_subclips>
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--hop_size <step_size_for_computing_volume_curve>
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```
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This is how dataset ASR processing is done using the command line(Only Chinese)
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```
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python tools/asr/funasr_asr.py -i <input> -o <output>
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```
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ASR processing is performed through Faster_Whisper(ASR marking except Chinese)
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(No progress bars, GPU performance may cause time delays)
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```
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python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
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```
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A custom list save path is enabled
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## Credits
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Special thanks to the following projects and contributors:
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### Theoretical Research
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- [ar-vits](https://github.com/innnky/ar-vits)
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- [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
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- [vits](https://github.com/jaywalnut310/vits)
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- [TransferTTS](https://github.com/hcy71o/TransferTTS/blob/master/models.py#L556)
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- [contentvec](https://github.com/auspicious3000/contentvec/)
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- [hifi-gan](https://github.com/jik876/hifi-gan)
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- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
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### Pretrained Models
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- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
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- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
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### Text Frontend for Inference
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- [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
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- [LangSegment](https://github.com/juntaosun/LangSegment)
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- [g2pW](https://github.com/GitYCC/g2pW)
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- [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
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- [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)
|
344 |
-
### WebUI Tools
|
345 |
-
- [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
|
346 |
-
- [audio-slicer](https://github.com/openvpi/audio-slicer)
|
347 |
-
- [SubFix](https://github.com/cronrpc/SubFix)
|
348 |
-
- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
|
349 |
-
- [gradio](https://github.com/gradio-app/gradio)
|
350 |
-
- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
|
351 |
-
- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
|
352 |
-
|
353 |
-
Thankful to @Naozumi520 for providing the Cantonese training set and for the guidance on Cantonese-related knowledge.
|
354 |
-
|
355 |
-
## Thanks to all contributors for their efforts
|
356 |
-
|
357 |
-
<a href="https://github.com/RVC-Boss/GPT-SoVITS/graphs/contributors" target="_blank">
|
358 |
-
<img src="https://contrib.rocks/image?repo=RVC-Boss/GPT-SoVITS" />
|
359 |
-
</a>
|
|
|
1 |
+
---
|
2 |
+
title: GPT SoVITS V2
|
3 |
+
emoji: 🐨
|
4 |
+
colorFrom: blue
|
5 |
+
colorTo: indigo
|
6 |
+
sdk: gradio
|
7 |
+
sdk_version: 4.40.0
|
8 |
+
app_file: app.py
|
9 |
+
pinned: false
|
10 |
+
license: mit
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|
11 |
---
|
12 |
|
13 |
+
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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