Matcha-TTS: A fast TTS architecture with conditional flow matching
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.
64f950dece75bb0fb5256790
Shivam Mehta
claimed_verified
2023-09-08T11:34:02.022Z
6034eda63d343696d96ef2ab
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Shivam Mehta
user
shivammehta25
64f950dece75bb0fb5256791
Ruibo Tu
64f950dece75bb0fb5256792
Jonas Beskow
64f950dece75bb0fb5256793
Éva Székely
64f950dece75bb0fb5256794
Gustav Eje Henter
Matcha-TTS: A fast TTS architecture with conditional flow matching
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.
Matcha-TTS is a new encoder-decoder architecture for text-to-speech that uses optimal-transport conditional flow matching to produce high-quality outputs quickly and efficiently, outperforming existing models in speed, memory usage, and audio quality.
Matcha-TTS encoder-decoder architecture optimal-transport conditional flow matching ODE-based decoder score matching non-autoregressive text-to-speech mean opinion score