AudioLDM AudioLDM was proposed in AudioLDM: Text-to-Audio Generation with Latent Diffusion Models by Haohe Liu et al. Inspired by Stable Diffusion, AudioLDM
is a text-to-audio latent diffusion model (LDM) that learns continuous audio representations from CLAP
latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional
sound effects, human speech and music. The abstract from the paper is: