Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models

TL;DR

Make-An-Audio introduces a prompt-enhanced diffusion model for text-to-audio generation, overcoming data scarcity and modeling challenges, achieving state-of-the-art results and enabling controllable, modality-agnostic audio synthesis.

Contribution

The paper presents a novel prompt-enhanced diffusion approach with pseudo prompt augmentation and spectrogram autoencoder, advancing text-to-audio generation and modality versatility.

Findings

Achieves state-of-the-art objective and subjective performance

Demonstrates controllability and generalization across modalities

Enables high-fidelity audio generation from various inputs

Abstract

Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data. In this work, we propose Make-An-Audio with a prompt-enhanced diffusion model that addresses these gaps by 1) introducing pseudo prompt enhancement with a distill-then-reprogram approach, it alleviates data scarcity with orders of magnitude concept compositions by using language-free audios; 2) leveraging spectrogram autoencoder to predict the self-supervised audio representation instead of waveforms. Together with robust contrastive language-audio pretraining (CLAP) representations, Make-An-Audio achieves state-of-the-art results in both objective and subjective benchmark evaluation. Moreover, we present its controllability and generalization for X-to-Audio with "No Modality Left Behind", for the first time unlocking the ability to generate high-definition, high-fidelity audios given a user-defined modality input. Audio samples are available at https://Text-to-Audio.github.io