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

• URL: http://arxiv.org/abs/2505.22106v1
• Date: Wed, 28 May 2025 08:33:58 GMT
• Title: AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion
• Authors: Junqi Zhao, Jinzheng Zhao, Haohe Liu, Yun Chen, Lu Han, Xubo Liu, Mark Plumbley, Wenwu Wang,
• Abstract summary: Rectified flow enhances inference speed by learning straight-line ordinary differential equation paths.<n>This approach requires training a flow-matching model from scratch and tends to perform suboptimally, or even poorly, at low step counts.<n>We propose AudioTurbo, which learns first-order ODE paths from deterministic noise sample pairs generated by a pre-trained TTA model.
• Score: 23.250409921931492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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