Fast Text-to-Audio Generation with Adversarial Post-Training

A new method accelerates text-to-audio generation using adversarial post-training. The approach refines a pretrained diffusion model with a discriminator, producing high-quality audio in fewer sampling steps. This technique reduces inference time while maintaining output fidelity, enabling faster real-time audio synthesis from text prompts.