Efficient Text-to-Audio Generation via Pruning
Diffusion-based text-to-audio generative models such as AudioLDM achieve high perceptual quality and strong semantic consistency; however, their practical deployment is hindered by the substantial computational cost of the U-Net denoising backbone. In this work, we apply model pruning to improve the computational efficiency of AudioLDM, a U-Net-based text-conditioned audio latent diffusion model. We analyse parameter redundancy across U-Net convolutional blocks and evaluate a filter-pruning strategy. Pruning is guided by norm-based criteria and followed by lightweight finetuning to recover performance losses. Experimental results demonstrate that up to 83% of the parameters and 39% of the...
| Authors | Arshdeep Singh, Yi Yuan, Yun Chen et al. |
|---|---|
| Primary category | eess.AS |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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