GPT-SoVITS-WebUI
A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.
Features:
- Zero-shot TTS: Input a 5-second vocal sample and experience instant text-to-speech conversion.
- Few-shot TTS: Fine-tune the model with just 1 minute of training data for improved voice similarity and realism.
- Cross-lingual Support: Inference in languages different from the training dataset, currently supporting English, Japanese, and Chinese.
- 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 here!
Unseen speakers few-shot fine-tuning demo:
few.shot.fine.tuning.demo.mp4
Installation
For users in China region, you can click here to use AutoDL Cloud Docker to experience the full functionality online.
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)
Note: numba==0.56.4 requires py<3.11
Windows
If you are a Windows user (tested with win>=10), you can directly download the pre-packaged distribution and double-click on go-webui.bat to start GPT-SoVITS-WebUI.
Linux
conda create -n GPTSoVits python=3.9 conda activate GPTSoVits bash install.sh
macOS
Only Macs that meet the following conditions can train models:
- Mac computers with Apple silicon
- macOS 12.3 or later
- Xcode command-line tools installed by running
xcode-select --install
All Macs can do inference with CPU, which has been demonstrated to outperform GPU inference.
First make sure you have installed FFmpeg by running brew install ffmpeg or conda install ffmpeg, then install by using the following commands:
conda create -n GPTSoVits python=3.9 conda activate GPTSoVits
pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu pip install -r requirements.txt
Note: Training models will only work if you've installed PyTorch Nightly.
Install Manually
Install Dependences
pip install -r requirements.txt
Install FFmpeg
Conda Users
Ubuntu/Debian Users
sudo apt install ffmpeg sudo apt install libsox-dev conda install -c conda-forge 'ffmpeg<7'
Windows Users
Download and place ffmpeg.exe and ffprobe.exe in the GPT-SoVITS root.
Using Docker
docker-compose.yaml configuration
- Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check Docker Hub for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs.
- 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.
- 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.
- 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.
- 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 9880:9880 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:xxxxx
Pretrained Models
Download pretrained models from GPT-SoVITS Models and place them in GPT_SoVITS/pretrained_models.
For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from 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"
For Chinese ASR (additionally), download models from Damo ASR Model, Damo VAD Model, and Damo Punc Model and place them in tools/damo_asr/models.
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:
- Localization in Japanese and English.
- User guide.
- 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.
- Colab scripts.
- Try expand training dataset (2k hours -> 10k hours).
- better sovits base model (enhanced audio quality)
- model mix
(Optional) If you need, here will provide the command line operation mode
Use the command line to open the WebUI for UVR5
python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
If you can't open a browser, follow the format below for UVR processing,This is using mdxnet for audio processing
python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
This is how the audio segmentation of the dataset is done using the command line
python audio_slicer.py \
--input_path "<path_to_original_audio_file_or_directory>" \
--output_root "<directory_where_subdivided_audio_clips_will_be_saved>" \
--threshold <volume_threshold> \
--min_length <minimum_duration_of_each_subclip> \
--min_interval <shortest_time_gap_between_adjacent_subclips>
--hop_size <step_size_for_computing_volume_curve>
This is how dataset ASR processing is done using the command line(Only Chinese)
python tools/damo_asr/cmd-asr.py "<Path to the directory containing input audio files>"
ASR processing is performed through Faster_Whisper(ASR marking except Chinese)
(No progress bars, GPU performance may cause time delays)
python ./tools/damo_asr/WhisperASR.py -i <input> -o <output> -f <file_name.list> -l <language>
A custom list save path is enabled
Credits
Special thanks to the following projects and contributors:
Thanks to all contributors for their efforts