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[**English**](./README.md) | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md)
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</div>
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------
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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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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
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## Features:
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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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## Environment Preparation
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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.
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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 2.3.0.dev20240122, macOS 14.3 (Apple silicon, GPU)
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_Note: numba==0.56.4 require py<3.11_
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### Quick Install with Conda
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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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### Install Manually
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#### Pip Packages
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```bash
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pip install -r requirements.txt
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```
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#### 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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##### MacOS Users
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```bash
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brew install ffmpeg
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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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### 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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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`.
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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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### For Mac Users
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If you are a Mac user, make sure you meet the following conditions for training and inferencing with GPU:
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- Mac computers with Apple silicon or AMD GPUs
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- macOS 12.3 or later
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- Xcode command-line tools installed by running `xcode-select --install`
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_Other Macs can do inference with CPU only._
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Then install by using the following commands:
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#### Create Environment
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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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```
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#### Install Requirements
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```bash
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pip install -r requirements.txt
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pip uninstall torch torchaudio
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pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
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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 9870:9870 -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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## 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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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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## Todo List
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- [ ] **High Priority:**
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- [x] Localization in Japanese and English.
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- [ ] User guide.
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- [x] Japanese and English dataset fine tune training.
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- [ ] **Features:**
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- [ ] Zero-shot voice conversion (5s) / few-shot voice conversion (1min).
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- [ ] 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 vocabs.
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- [ ] 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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- [ ] better sovits base model (enhanced audio quality)
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- [ ] model mix
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## Credits
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Special thanks to the following projects and contributors:
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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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- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
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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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- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
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- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
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- [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
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- [audio-slicer](https://github.com/openvpi/audio-slicer)
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- [SubFix](https://github.com/cronrpc/SubFix)
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- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
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- [gradio](https://github.com/gradio-app/gradio)
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## Thanks to all contributors for their efforts
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<a href="https://github.com/RVC-Boss/GPT-SoVITS/graphs/contributors" target="_blank">
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<img src="https://contrib.rocks/image?repo=RVC-Boss/GPT-SoVITS" />
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</a>
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---
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title: GPT-SoVITS KusanagiNene
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emoji: 🏃
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colorFrom: green
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colorTo: red
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sdk: gradio
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sdk_version: 4.16.0
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app_file: app.py
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pinned: false
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license: gpl-3.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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