6 papers
SAME: A Semantically-Aligned Music Autoencoder
Julian D. Parker, Zach Evans, CJ Carr +4
Latent representations are at the heart of the majority of modern generative models. In the audio domain they are typically produced by a neural-audio-codec autoencoder. In this wo…
Stable Audio 3
Zach Evans, Julian D. Parker, Matthew Rice +4
Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable-length audio generation and editing. Since our models can generate several minutes of…
Low-Resource Guidance for Controllable Latent Audio Diffusion
Zachary Novack, Zack Zukowski, CJ Carr +6
Generative audio requires fine-grained controllable outputs, yet most existing methods require model retraining on specific controls or inference-time controls (\textit{e.g.}, guid…
Music and Artificial Intelligence: Artistic Trends
Jordi Pons, Zack Zukowski, Julian D. Parker +3
We study how musicians use artificial intelligence (AI) across formats like singles, albums, performances, installations, voices, ballets, operas, or soundtracks. We collect 337 mu…
Fast Text-to-Audio Generation with Adversarial Post-Training
Zachary Novack, Zach Evans, Zack Zukowski +8
Text-to-audio systems, while increasingly performant, are slow at inference time, thus making their latency unpractical for many creative applications. We present Adversarial Relat…
Scaling Transformers for Low-Bitrate High-Quality Speech Coding
Julian D Parker, Anton Smirnov, Jordi Pons +4
The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Tr…