Reflow-tts: A Rectified Flow Model For High-fidelity Text-to-speech

Abstract

The diffusion models including Denoising Diffusion Probabilistic Models (DDPM) and score-based generative models have demonstrated excellent performance in speech synthesis tasks. However, its effectiveness comes at the cost of numerous sampling steps, resulting in prolonged sampling time required to synthesize high-quality speech. This drawback hinders its practical applicability in real-world scenarios. In this paper, we introduce ReFlow-TTS, a novel rectified flow based method for speech synthesis with high-fidelity. Specifically, our ReFlow-TTS is simply an Ordinary Differential Equation (ODE) model that transports Gaussian distribution to the ground-truth Mel-spectrogram distribution by straight line paths as much as possible. Furthermore, our proposed approach enables high-quality speech synthesis with a single sampling step and eliminates the need for training a teacher model. Our experiments on LJSpeech Dataset show that our ReFlow-TTS method achieves the best performance compa

Stats

• citations9
• S2 citations—
• github stars0
• HF likes0
• heat score7.50
• arxiv keyguan2023reflow

Related papers

• Voiceflow: Efficient Text-to-speech With Rectified Flow Matching (2023)0.00
• F5-TTS: A Fairytaler That Fakes Fluent And Faithful Speech With Flow Matching (2024)0.00
• Prodiff: Progressive Fast Diffusion Model For High-quality Text-to-speech (2022)0.00
• Schrodinger Bridges Beat Diffusion Models On Text-to-speech Synthesis (2023)0.00
• Diffgan-tts: High-fidelity And Efficient Text-to-speech With Denoising Diffusion Gans (2022)0.00
• Resgrad: Residual Denoising Diffusion Probabilistic Models For Text To Speech (2022)0.00
• Diflow-tts: Compact And Low-latency Zero-shot Text-to-speech With Factorized Discrete Flow Matching (2025)0.00
• Flashaudio: Rectified Flows For Fast And High-fidelity Text-to-audio Generation (2024)5.13