Matcha-TTS: A fast TTS architecture with conditional flow matching
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
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.
Key facts. Cited by 142 papers in the map (data through 7 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 35 of its 43 references that are in the map. First posted on arXiv 6 Sep 2023.
Builds on
35 of 43 references.
Closest ancestors · 8
Same direction, newest first
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- 2021Diff-TTS: A Denoising Diffusion Model for Text-to-Speech3 Apr
- 2020DiffWave: A Versatile Diffusion Model for Audio Synthesis21 Sep
Other references · 27
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
- 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
- 2018Self-Attention with Relative Position Representationscited by 1,112
- 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,085
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- 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 710
- 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
- 2023Voicebox: Text-Guided Multilingual Universal Speech Generation at Scalecited by 373
- 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 308
- 2020Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Searchcited by 269
- 2022BigVGAN: A Universal Neural Vocoder with Large-Scale Trainingcited by 245
- 2021WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesiscited by 65
- 2022EmoDiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidancecited by 37
- 2021The Case for Translation-Invariant Self-Attention in Transformer-Based Language Modelscited by 23
- 2023Diff-TTSG: Denoising probabilistic integrated speech and gesture synthesiscited by 13
- 2023Why We Should Report the Details in Subjective Evaluation of TTS More Rigorouslycited by 10
- 2021Neural HMMs are all you need (for high-quality attention-free TTS)cited by 8
- 2022OverFlow: Putting flows on top of neural transducers for better TTScited by 8
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Citing papers per year: 2023: 1; 2024: 20; 2025: 69; 2026: 52.
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