AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908382715183104 |
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| author | Zhao, Junqi Zhao, Jinzheng Liu, Haohe Chen, Yun Han, Lu Liu, Xubo Plumbley, Mark Wang, Wenwu |
| author_facet | Zhao, Junqi Zhao, Jinzheng Liu, Haohe Chen, Yun Han, Lu Liu, Xubo Plumbley, Mark Wang, Wenwu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22106 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion Zhao, Junqi Zhao, Jinzheng Liu, Haohe Chen, Yun Han, Lu Liu, Xubo Plumbley, Mark Wang, Wenwu Sound Artificial Intelligence Audio and Speech Processing 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. |
| title | AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.22106 |