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arXiv · 2505.19931

Accelerating Flow-Matching-Based Text-to-Speech via Empirically Pruned Step Sampling

Qixi Zheng·Yushen Chen·Zhikang Niu·Ziyang Ma·Xiaofei Wang·Kai Yu·Xie Chen
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Abstract

Flow-matching-based text-to-speech (TTS) models, such as Voicebox, E2 TTS, and F5-TTS, have attracted significant attention in recent years. These models require multiple sampling steps to reconstruct speech from noise, making inference speed a key challenge. Reducing the number of sampling steps can greatly improve inference efficiency. To this end, we introduce Fast F5-TTS, a training-free approach to accelerate the inference of flow-matching-based TTS models. By inspecting the sampling trajectory of F5-TTS, we identify redundant steps and propose Empirically Pruned Step Sampling (EPSS), a non-uniform time-step sampling strategy that effectively reduces the number of sampling steps. Our approach achieves a 7-step generation with an inference RTF of 0.030 on an NVIDIA RTX 3090 GPU, making it 4 times faster than the original F5-TTS while maintaining comparable performance. Furthermore, EPSS performs well on E2 TTS models, demonstrating its strong generalization ability.

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Qixi Zheng, Yushen Chen, Zhikang Niu, Ziyang Ma, Xiaofei Wang, Kai Yu, Xie Chen. 2025-05-26. Accelerating Flow-Matching-Based Text-to-Speech via Empirically Pruned Step Sampling. https://arxiv.org/abs/2505.19931

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