title: Matcha-TTS: A fast TTS architecture with conditional flow matching
summary: 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.
paper_authors._id: 64f950dece75bb0fb5256790
paper_authors.name: Shivam Mehta
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-08T11:34:02.022Z
paper_authors._id: 6034eda63d343696d96ef2ab
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1614081444819-noauth.jpeg
paper_authors.fullname: Shivam Mehta
paper_authors.type: user
paper_authors.user: shivammehta25
paper_authors._id: 64f950dece75bb0fb5256791
paper_authors.name: Ruibo Tu
paper_authors._id: 64f950dece75bb0fb5256792
paper_authors.name: Jonas Beskow
paper_authors._id: 64f950dece75bb0fb5256793
paper_authors.name: Éva Székely
paper_authors._id: 64f950dece75bb0fb5256794
paper_authors.name: Gustav Eje Henter
paper_title: Matcha-TTS: A fast TTS architecture with conditional flow matching
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Matcha-TTS encoder-decoder architecture optimal-transport conditional flow matching ODE-based decoder score matching non-autoregressive text-to-speech mean opinion score