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  1. README.md +8 -8
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@@ -5,10 +5,10 @@ tags:
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  - SpeechEnhancement
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  ---
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- # ENOT-AutoDL MP-SENet optimization on VoiceBank+DEMAND dataset.
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  This repository contains the optimized version of [MP-SENet](https://github.com/yxlu-0102/MP-SENet) model.
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- Number of MACs aka FLOPS was used for computational complexity measurement. PESQ score was used as a quality metric.
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  ## Optimization results
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@@ -16,16 +16,16 @@ We use MACs as a latency measure because this metric is device-agnostic and impl
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  There is also a possibility to optimize a model by target device latency using ENOT neural architecture selection algorithm.
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  Please, keep in mind that acceleration by device latency differs from acceleration by MACs.
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- | **Model** | **MACs** | **acceleration** | PESQ score |
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- |----------------|:--------:|:----------------:|:----------:|
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- | baseline | 302.39 B | 1.0 | 3.381 |
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- | ENOT optimized | 120.95 B | 2.5 | 3.386 |
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- You can use `Baseline_model.pth` and `ENOT_optimized_model.pth` in the original repo by loading them as generator in the following way:
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  ```python
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  generator = torch.load("ENOT_optimized_model.pth")
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  ```
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- There are model objects, saved by `torch.save`, so you can load them only from the original repository root because of imports.
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  If you want to book a demo, please contact us: [email protected] .
 
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  - SpeechEnhancement
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  ---
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+ # MP-SENet optimization on VoiceBank+DEMAND dataset with ENOT-AutoDL.
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  This repository contains the optimized version of [MP-SENet](https://github.com/yxlu-0102/MP-SENet) model.
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+ Number of multiplication and addition operations (MACs) was used for computational complexity measurement. PESQ score was used as a quality metric.
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  ## Optimization results
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  There is also a possibility to optimize a model by target device latency using ENOT neural architecture selection algorithm.
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  Please, keep in mind that acceleration by device latency differs from acceleration by MACs.
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+ | **Model** | **MACs** | **Acceleration (MACs)** | PESQ score (the higher the better) |
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+ |----------------|:--------:|:-----------------------:|:----------------------------------:|
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+ | baseline | 302.39 B | 1.0 | 3.381 |
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+ | ENOT optimized | 120.95 B | 2.5 | 3.386 |
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+ You can use `Baseline_model.pth` and `ENOT_optimized_model.pth` in the original repo by loading a model as generator in the following way:
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  ```python
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  generator = torch.load("ENOT_optimized_model.pth")
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  ```
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+ These two files contains a model objects, saved by `torch.save`, so you can load them only from the original repository root because of imports.
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  If you want to book a demo, please contact us: [email protected] .