Diffusion Models for Audio Generation

Module: Generative AI | Difficulty: Advanced

Audio Diffusion

AudioLDM

class AudioDiffusion(nn.Module):
    def __init__(self, unet, vae, clap):
        super().__init__()
        self.unet, self.vae, self.clap = unet, vae, clap
    def forward(self, mel, text, t):
        z = self.vae.encode(mel)
        cond = self.clap.encode_text(text)
        noise = torch.randn_like(z)
        return ((noise - self.unet(z+t*noise, t, cond))**2).mean()

Research Insight: Audio diffusion models struggle with temporal coherence. Solutions include: hierarchical diffusion (coarse-to-fine), autoregressive latent diffusion, and consistency models for real-time generation.