This repository contains the optimized version of MP-SENet model. Number of multiplication and addition operations (MACs) was used for computational complexity measurement. PESQ score was used as a quality metric.

We use MACs as a latency measure because this metric is device-agnostic and implementation independent.
There is also a possibility to optimize a model by target device latency using ENOT neural architecture selection algorithm.
Please, keep in mind that acceleration by device latency differs from acceleration by MACs.

You can use Baseline_model.pth or ENOT_optimized_model.pth in the original repo by loading a model as generator in the following way:

Each of these two files contain a model object, saved by torch.save, so you can load them only from the original repository root because of imports.

If you want to book a demo, please contact us: enot@enot.ai .