Fast Timing-Conditioned Latent Audio Diffusion
Authors: name: Zach Evans, name: CJ Carr, name: Josiah Taylor, name: Scott H. Hawley, name: Jordi Pons

Generating long-form 44.1kHz stereo audio from text prompts can be
computationally demanding. Further, most previous works do not tackle that
music and sound effects naturally vary in their duration. Our research focuses
on the efficient generation of long-form, variable-length stereo music and
sounds at 44.1kHz using text prompts with a generative model. Stable Audio is
based on latent diffusion, with its latent defined by a fully-convolutional
variational autoencoder. It is conditioned on text prompts as well as timing
embeddings, allowing for fine control over both the content and length of the
generated music and sounds. Stable Audio is capable of rendering stereo signals
of up to 95 sec at 44.1kHz in 8 sec on an A100 GPU. Despite its compute
efficiency and fast inference, it is one of the best in two public
text-to-music and -audio benchmarks and, differently from state-of-the-art
models, can generate music with structure and stereo sounds.