Downloads · 30 days
0
MP-SENet
ENOT-AutoDL/MP-SENet
MP-SENet is a machine learning model from ENOT-AutoDL. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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.
Access
Public
Updated Mar 26, 2024
Repo size
22 MB
Likes
7
Public
Repo makeup
Click a slice to open those files.
.onnx11.1 MB · 50%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Mar 25, 2024
- Updated
- Mar 26, 2024
- SHA
- d1c182e4
Model card
From the Hugging Face model README
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 .
- License
- apache-2.0
- Created
- Mar 25, 2024
- Updated
- Mar 26, 2024