HARP 2.0: Expanding Hosted, Asynchronous, Remote Processing for Deep Learning in the DAW
This work addresses the problem of bridging model developers and creatives by improving access to deep learning models in DAW workflows, though it is incremental as an expansion of an existing system.
The paper tackles integrating deep learning models into digital audio workstations (DAW) by developing HARP 2.0, a system that allows users to route audio through Gradio endpoints for transformations without leaving the DAW, resulting in support for MIDI-based and audio/MIDI labeling models with interface and stability improvements.
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 integrating them into DAW workflows.