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: [email protected] .