DCUNetTorchSound

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DCUNetTorchSound

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Implementation of Phase-aware speech enhancement with deep complex U-Net

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# Phase-aware speech enhancement with DC U-Net
Implementation of paper [Phase-aware speech enhancement with deep complex U-Net](https://openreview.net/pdf?id=SkeRTsAcYm)

### Train
Here you find all 4 architectires from paper

![DCUnet_10](https://raw.githubusercontent.com/mhlevgen/DCUNetTorchSound/master/images/DCUnet_10.png)

```bash
python3 train_unet.py -m_f 32 -e_d 5 -epochs 10
```

![DCUnet_16](https://raw.githubusercontent.com/mhlevgen/DCUNetTorchSound/master/images/DCUnet_16.png)

```bash
python3 train_unet.py -m_f 32 -e_d 8 -epochs 10
```

![DCUnet_20](https://raw.githubusercontent.com/mhlevgen/DCUNetTorchSound/master/images/DCUnet_20.png)

```bash
python3 train_unet.py -m_f 32 -e_d 10 -epochs 10
```

![DCUnet_20](https://raw.githubusercontent.com/mhlevgen/DCUNetTorchSound/master/images/Large_DCUnet_20.png)

```bash
python3 train_unet.py -m_f 45 -e_d 10 -epochs 10
```

Model is saved after every epoch if save_best = False, if save_best=True
model is saved only if PESQ on val data increased.
Specify checkpoint name in -from_checkpoint to start training from checkpoint

### Inference
Option 1: inference from Voice Bank + DEMAND with specified voice and noise and 
desired SNR

```bash
python3 inference_one_audio.py \
-chp chp_model_32_8_epoch_5_-0.97_2.98.pth \
-srn 0 \
-speaker_id p295 \
-utterance_id p295_168.wav \
-noise_origin SCAFE \
-noise_id ch14.wav
```
speaker_id, utterance_id, noise_origin, noise_id - can be None, if None
all of them will be random

chp - choose checkpoint name from 'models' directory. All checkpoints during training will be saved in
'models' directory

Option 2: inference from custom file
```bash
python3 inference_one_audio.py \
-chp chp_model_32_10_epoch_3_-0.98_2.99.pth \
-custom_file results/live_1.wav \
```
-custom_file - path to custom file to read and process in model

### Some experiments (after 10 epochs training)

|  SNR          |  Initial sound| DCUnet-10     | DCUnet-16  | DCUnet-20 |
| :-------------|-------------:| -------------:|-----------:|----------:|
| live audio    | [live.wav](results/live_1.wav) |[live_10.wav](results/model_32_5_9_live_1.wav) | [live_16.wav](results/model_32_8_6_live_1.wav) |[live_20.wav](results/model_32_10_6_live_1.wav)|
| 0             | [init_sound_1.wav](results/init_sound_p361_010_TMETRO_ch01_0.wav) |[sound_1_10.wav](results/model_32_5_9_p361_010_TMETRO_ch01_0.wav) | [sound_1_16.wav](results/model_32_8_9_p361_010_TMETRO_ch01_0.wav) |[sound_1_20.wav](results/model_32_10_6_p361_010_TMETRO_ch01_0.wav)|
| 10            | [init_sound_2.wav](results/init_sound_p361_010_TMETRO_ch01_10.wav) |[sound_2_10.wav](results/model_32_5_9_p361_010_TMETRO_ch01_10.wav) | [sound_2_16.wav](results/model_32_8_9_p361_010_TMETRO_ch01_10.wav) |[sound_2_20.wav](results/model_32_10_6_p361_010_TMETRO_ch01_10.wav)|

DCUNetTorchSound

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