Matcha-TTS: A fast TTS architecture with conditional flow matching
Authors: name: Shivam Mehta, name: Ruibo Tu, name: Jonas Beskow, name: Éva Székely, name: Gustav Eje Henter

We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS
acoustic modelling, trained using optimal-transport conditional flow matching
(OT-CFM). This yields an ODE-based decoder capable of high output quality in
fewer synthesis steps than models trained using score matching. Careful design
choices additionally ensure each synthesis step is fast to run. The method is
probabilistic, non-autoregressive, and learns to speak from scratch without
external alignments. Compared to strong pre-trained baseline models, the
Matcha-TTS system has the smallest memory footprint, rivals the speed of the
fastest models on long utterances, and attains the highest mean opinion score
in a listening test. Please see https://shivammehta25.github.io/Matcha-TTS/ for
audio examples, code, and pre-trained models.