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Decision brief
GPT-SoVITS offers text-to-speech with minimal voice data via few-shot learning, suited for limited-data scenarios.
Good fit when
- You need to train a TTS model with less than one minute of voice samples
- Your project requires low-resource initialization or fine-tuning capabilities
Avoid when
- If you require high-quality speech synthesis from extensive preexisting datasets and training
- When operating environments lack GPU support for half-precision, as optimal performance relies on it
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (6d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- 39 low (39 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install GPT-SoVITSHow it fits your stack(6)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A tool for training text-to-speech (TTS) models using very limited voice data through few-shot learning techniques.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 29, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 29, 2026
- Languages
- python
Source: github.language · Jul 29, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 29, 2026)
conda create -n GPTSoVits python=3.10Source link
Tags
README
Installation
For users in China, you can click here to use AutoDL Cloud Docker to experience the full functionality online.
Install Manually
Install Dependences
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
conda activate GPTSoVits
conda install ffmpeg
Ubuntu/Debian Users
sudo apt install ffmpeg
sudo apt install libsox-dev
Windows Users
Download and place ffmpeg.exe and ffprobe.exe in the GPT-SoVITS root
Install Visual Studio 2017
MacOS Users
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 for the latest available image tags
- Choose an appropriate image tag for your environment
Litemeans 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 totrueif 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:
docker compose run --service-ports <GPT-SoVITS-CU126-Lite|GPT-SoVITS-CU128-Lite|GPT-SoVITS-CU126|GPT-SoVITS-CU128>
Building the Docker Image Locally
If you want to build the image yourself, use:
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:
docker exec -it <GPT-SoVITS-CU126-Lite|GPT-SoVITS-CU128-Lite|GPT-SoVITS-CU126|GPT-SoVITS-CU128> bash
For agents
This page has a .md twin and JSON over the API.