Matcha-TTS is a non-autoregressive neural TTS system that uses conditional flow matching to synthesize speech from text. The model learns a probability path between noise and audio through diffusion-style training, and performs inference by solving an ODE to generate waveforms. The system is designed to be fast, probabilistic, and memory-efficient while producing natural-sounding speech, published at ICASSP 2024.