Live Music Models
Lyria Team: Antoine Caillon, Brian McWilliams, Cassie Tarakajian, Ian Simon, Ilaria Manco, Jesse Engel, Noah Constant, Yunpeng Li, Timo I. Denk, Alberto Lalama, Andrea Agostinelli, Cheng-Zhi Anna Huang, Ethan Manilow, George Brower, Hakan Erdogan, Heidi Lei, Itai Rolnick

TL;DR
This paper presents Magenta RealTime and Lyria RealTime, innovative live music generation models enabling real-time, user-controlled music creation with superior quality and interactive capabilities, advancing AI-assisted live music performance.
Contribution
Introduction of live music models that generate continuous, real-time music with synchronized user control, outperforming existing models in quality and offering new live performance features.
Findings
Magenta RealTime outperforms other open-weights models on quality metrics.
Models enable real-time, live music generation with user control.
Lyria RealTime provides extensive control and wide prompt coverage.
Abstract
We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release Magenta RealTime, an open-weights live music model that can be steered using text or audio prompts to control acoustic style. On automatic metrics of music quality, Magenta RealTime outperforms other open-weights music generation models, despite using fewer parameters and offering first-of-its-kind live generation capabilities. We also release Lyria RealTime, an API-based model with extended controls, offering access to our most powerful model with wide prompt coverage. These models demonstrate a new paradigm for AI-assisted music creation that emphasizes human-in-the-loop interaction for live music performance.
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Code & Models
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