NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects

We present NablAFx, an open-source framework developed to support research in differentiable black-box and gray-box modeling of audio effects. Built in PyTorch, NablAFx offers a versatile ecosystem to configure, train, evaluate, and compare various architectural approaches. It includes classes to manage model architectures, datasets, and training, along with features to compute and log losses, metrics and media, and plotting functions to facilitate detailed analysis. It incorporates implementations of established black-box architectures and conditioning methods, as well as differentiable DSP blocks and controllers, enabling the creation of both parametric and non-<PRE_TAG>parametric</POST_TAG> gray-box signal chains. The code is accessible at https://github.com/mcomunita/nablafx.