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arXiv · 2505.22106

AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion

Junqi Zhao·Jinzheng Zhao·Haohe Liu·Yun Chen·Lu Han·Xubo Liu·Mark Plumbley·Wenwu Wang
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Abstract

Diffusion models have significantly improved the quality and diversity of audio generation but are hindered by slow inference speed. Rectified flow enhances inference speed by learning straight-line ordinary differential equation (ODE) paths. However, this approach requires training a flow-matching model from scratch and tends to perform suboptimally, or even poorly, at low step counts. To address the limitations of rectified flow while leveraging the advantages of advanced pre-trained diffusion models, this study integrates pre-trained models with the rectified diffusion method to improve the efficiency of text-to-audio (TTA) generation. Specifically, we propose AudioTurbo, which learns first-order ODE paths from deterministic noise sample pairs generated by a pre-trained TTA model. Experiments on the AudioCaps dataset demonstrate that our model, with only 10 sampling steps, outperforms prior models and reduces inference to 3 steps compared to a flow-matching-based acceleration model.

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Junqi Zhao, Jinzheng Zhao, Haohe Liu, Yun Chen, Lu Han, Xubo Liu, Mark Plumbley, Wenwu Wang. 2025-05-28. AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion. https://arxiv.org/abs/2505.22106

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