MP-SENet optimization on VoiceBank+DEMAND dataset with ENOT-AutoDL.

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.

Optimization results

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.

Model	MACs	Acceleration (MACs)	PESQ score (the higher the better)
baseline	302.39 B	1.0	3.381
ENOT optimized	120.95 B	2.5	3.386

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:

generator = torch.load("ENOT_optimized_model.pth")

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 .