Matcha-TTS is a fast, non-autoregressive text-to-speech (TTS) architecture designed for natural-sounding speech synthesis. It targets researchers and developers seeking efficient TTS solutions, offering probabilistic generation with a compact memory footprint and rapid synthesis times.

How It Works

Matcha-TTS employs conditional flow matching, a technique inspired by rectified flows, to accelerate ODE-based speech synthesis. This probabilistic approach models the transformation from noise to speech, enabling faster inference compared to traditional autoregressive models while maintaining high audio quality.

Quick Start & Requirements

• Install via pip: pip install matcha-tts or from source.
• Requires Python 3.10 and PyTorch 2.0+.
• Pre-trained models are downloaded automatically.
• Demo available on HuggingFace Spaces.

Highlighted Details

• Utilizes conditional flow matching for fast, non-autoregressive TTS.
• Probabilistic generation with a compact memory footprint.
• Achieves highly natural-sounding speech.
• Supports ONNX export and inference for deployment.

Maintenance & Community

The project is associated with KTH Royal Institute of Technology. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license. Users should verify licensing for commercial or closed-source use.

Limitations & Caveats

ONNX export requires PyTorch >= 2.1.0 due to specific operator exportability. Users needing to export models must manually install this version. The project is presented as the official implementation for an ICASSP 2024 paper.