The Resing Modeler allow to create a fully functional ReSing Model from a set of vocal or instrument recording. This processing known as modeling involves analyzing and learning the unique features of a voice/instrument through a series of machine learning training steps, during which the software processes each recording to extract detailed acoustic information and generate a model capable of reproducing that voice/instrument with high fidelity.

Because the training phase relies on complex deep learning computations, hardware performance plays a crucial role in determining both the speed and quality of the results.

On macOS, Intel-based systems represent the least efficient option for voice modeling, as the process relies exclusively on CPU computation. Since traditional CPUs are not optimized for the parallel processing required by deep learning, training performance is significantly limited.

In contrast, Apple Silicon architectures (M1, M2, M3, etc.) deliver a major improvement thanks to Metal Performance Shaders (MPS), which allow the Modeler to harness the GPU and Neural Engine for accelerated processing.

On Windows, CPU-only configurations face similar limitations to those seen on Intel-based Macs, however, when equipped with an NVIDIA GPU* supporting CUDA*, Windows systems achieve the best overall performance. CUDA acceleration enables massive parallelization, allowing the Modeler to train even complex, high-quality voice models with exceptional speed and stability.