F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
This paper introduces F5-TTS, a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT). Without requiring complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then the denoising is performed for speech generation, which was originally proved feasible by E2 TTS. However, the original design of E2 TTS makes it hard to follow due to its slow convergence and low robustness. To address these issues, we first model the input with ConvNeXt to refine the text representation, making it easy to align with the speech. We further propose an inference-time Sway Sampling strategy, which significantly improves our model's performance and efficiency. This sampling strategy for flow step can be easily applied to existing flow matching based models without retraining. Our design allows faster training and achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based TTS models. Trained on a public 100K hours multilingual dataset, our F5-TTS exhibits highly natural and expressive zero-shot ability, seamless code-switching capability, and speed control efficiency. We have released all codes and checkpoints to promote community development, at https://SWivid.github.io/F5-TTS/.
Key facts. Cited by 361 papers in the map (data through 14 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 68 of its 76 references that are in the map. First posted on arXiv 9 Oct 2024.
Builds on
68 of 76 references.
Closest ancestors · 11
Same direction, newest first
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- 2023NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers18 Apr
- 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech13 May
Other references · 57
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
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- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
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- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2022A ConvNet for the 2020scited by 3,579
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
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- 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 649
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