Applio

Overview

Applio is a free, open-source, AI-driven voice conversion suite developed by the IAHispano community. It provides a complete pipeline for transforming any audio into another voice using deep learning-based voice models, including support for architectures such as RVC v2. The project is MIT-licensed, requires no accounts or paywalls, and collects zero telemetry—all processing runs locally on the user's hardware, ensuring privacy and full data control. The suite is available natively on Windows, Linux, and macOS, with additional deployment options on Google Colab and Kaggle for cloud GPU acceleration, plus an in-browser playground for instant experimentation.

At its core, Applio delivers high-quality voice conversion using ready-made voice models. Users can tune pitch, clean, upscale, and batch-process audio files effortlessly. For live scenarios, the real-time voice changer achieves low-latency inference of under 120 milliseconds, making it suitable for singing, streaming, and voice calls. The platform also features a complete model training workflow: users can pre-process their datasets, extract features, train, and export custom voice models in just a few steps. Additional modules include a Voice Blender that merges multiple voice models into one unique new voice with fine-grained control over each contributor, a Text-to-Speech engine that routes generated speech through voice models, and an Audio Analyzer that provides detailed audio health information to help users prepare and validate datasets before training.

Technically, Applio is designed for accessibility. Installation requires no prerequisites, as all dependencies are bundled into a single install script for Windows, Linux, or macOS. For best training performance, an Nvidia RTX 20-series GPU or newer is recommended, while a modern computer is sufficient for inference in most cases. The project is fully transparent: source code, roadmap, issues, and releases are public on GitHub, where the repository has accumulated more than 3,600 stars, over 580 forks, and contributions from more than 145 developers. Every change is publicly reviewed, and the MIT license permits use, modification, and redistribution for personal projects, academic research, or commercial applications.

Applio serves a wide spectrum of users, from complete beginners to seasoned audio professionals. Musicians and content creators can produce AI covers from songs, streamers can transform their voice in real time during broadcasts, and researchers can explore voice conversion and synthesis techniques. Since no account, telemetry, or paid plan is required, Applio is particularly attractive for privacy-conscious individuals and organizations. The ecosystem is sustained by an active community on Discord and GitHub, with ongoing development, a publicly maintained blog, and support channels that keep the project free forever by design.

Core capabilities

Voice Conversion

Convert any audio into another voice using ready-made voice models, with pitch tuning, audio cleaning, upscaling, and batch-processing capabilities.

Real-Time Voice Change

Transform your voice live with low-latency inference under 120 ms, ideal for songs, streams, and calls.

Model Training

Train custom voice models from your own dataset through pre-processing, feature extraction, training, and export in just a few steps.

Voice Blender

Merge multiple voice models into a single unique voice with full control over each contributing model.

Text-to-Speech

Generate speech from text and route it through your voice models to create custom TTS output.

Audio Analyzer

Check dataset readiness with detailed audio health information before training.

Cross-Platform Deployment

Run natively on Windows, Linux, and macOS, or leverage free GPU time on Google Colab and Kaggle; an in-browser playground is also available.

Open Source & MIT Licensed

Freely use, modify, and redistribute the code for personal projects, research, or commercial work.

Privacy by Design

No accounts, no telemetry, and no data collection—everything runs locally on your machine.

Use cases

Creating AI covers by converting existing songs into another voice

Live voice changing during streams, online calls, and gaming sessions

Training a custom voice model from personal or licensed audio datasets

Blending several voice models to synthesize a brand-new voice

Generating text-to-speech audio and passing it through custom voice models

Validating and preparing audio datasets with the Audio Analyzer before training

Batch-processing audio files with pitch tuning, cleaning, and upscaling