  Deep neural networks have recently achieved breakthroughs in sound
generation. Despite the outstanding sample quality, current sound generation
models face issues on small-scale datasets (e.g., overfitting), significantly
limiting performance. In this paper, we make the first attempt to investigate
the benefits of pre-training on sound generation with AudioLDM, the
cutting-edge model for audio generation, as the backbone. Our study
demonstrates the advantages of the pre-trained AudioLDM, especially in
data-scarcity scenarios. In addition, the baselines and evaluation protocol for
sound generation systems are not consistent enough to compare different studies
directly. Aiming to facilitate further study on sound generation tasks, we
benchmark the sound generation task on various frequently-used datasets. We
hope our results on transfer learning and benchmarks can provide references for
further research on conditional sound generation.
