Overview
Applio is a free, open-source voice conversion suite for converting speech or singing, creating AI covers, training custom voice models, and experimenting with real-time voice transformation.
What Applio does
Applio is a free, open-source voice conversion suite built around Retrieval-Based Voice Conversion. It can transform recorded speech or singing with a selected voice model while retaining much of the original phrasing, pitch movement, and delivery.
Voice conversion and model training
Users can run inference with existing voice models, process individual files or batches, and adjust pitch, index influence, audio cleaning, and export settings. Applio also includes tools for preparing datasets and training custom voice models from suitable source audio.
Who Applio is useful for
The software is aimed at creators experimenting with AI covers, voice conversion, speech transformation, and custom model development. Beginners can use its graphical interface, while technical users can work through its command-line tools, local installation options, Docker images, or cloud notebooks.
Important considerations
Local model training benefits from capable NVIDIA hardware, although inference with existing models can run on many modern computers. Voice cloning and conversion should only be performed with appropriate permission and in ways that respect identity, consent, copyright, and applicable platform rules.
Key features
- Voice conversion with pre-trained RVC models
- Single-file, batch, and real-time inference
- Custom voice model training
- Dataset creation and audio analysis
- Built-in text-to-speech workflow
- Voice model blending
- Graphical interface and command-line tools
- Local, Docker, Colab, and Kaggle deployment options
Best for
Creators, musicians, researchers, and technical users exploring consent-based AI voice conversion or custom voice-model workflows.
Use cases
- Convert recorded speech or singing with a selected voice model
- Create AI-assisted song covers using authorized material
- Train a custom voice model from a prepared dataset
- Process multiple audio files in a batch
- Experiment with live voice conversion
- Generate speech with TTS and pass it through a voice model
Pros
- Free and open source under the MIT License
- Supports both inference and custom model training
- Runs locally with no account required
- Offers graphical and command-line workflows
- Includes audio preparation and analysis tools
Considerations
- Local model training benefits from a capable NVIDIA GPU
- Installation and model training require more setup than a hosted web service
- Output quality depends on the source audio, dataset, model, and configuration
- Users must respect consent, identity rights, copyright, and platform policies