HARP 2.0: Expanding Hosted, Asynchronous, Remote Processing for Deep Learning in the DAW
Christodoulos Benetatos (University of Rochester), Frank Cwitkowitz (University of Rochester), Nathan Pruyne (Northwestern University), Hugo Flores García (Northwestern University), Patrick O'Reilly (Northwestern University)*, Zhiyao Duan (Unversity of Rochester), Bryan Pardo (Northwestern University)
This paper will be presented in person
HARP 2.0 brings deep learning models to digital audio workstation (DAW) software through hosted, asynchronous, remote processing, allowing users to route audio from a plug-in interface through any compatible Gradio endpoint to perform arbitrary transformations. HARP renders endpoint-defined controls and processed audio in-plugin, meaning users can explore a variety of cutting-edge deep learning models without ever leaving the DAW. In the 2.0 release we introduce support for MIDI-based models and audio/MIDI labeling models, provide a streamlined pyharp Python API for model developers, and implement numerous interface and stability improvements. Through this work, we hope to bridge the gap between model developers and creatives, improving access to deep learning models by seamlessly intrgrating them into DAW workflows.