Implementation of paper Phase-aware speech enhancement with deep complex U-Net
Here you find all 4 architectires from paper
python3 train_unet.py -m_f 32 -e_d 5 -epochs 10
python3 train_unet.py -m_f 32 -e_d 8 -epochs 10
python3 train_unet.py -m_f 32 -e_d 10 -epochs 10
python3 train_unet.py -m_f 45 -e_d 10 -epochs 10Model 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
Option 1: inference from Voice Bank + DEMAND with specified voice and noise and desired SNR
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.wavspeaker_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
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
| SNR | Initial sound | DCUnet-10 | DCUnet-16 | DCUnet-20 |
|---|---|---|---|---|
| live audio | live.wav | live_10.wav | live_16.wav | live_20.wav |
| 0 | init_sound_1.wav | sound_1_10.wav | sound_1_16.wav | sound_1_20.wav |
| 10 | init_sound_2.wav | sound_2_10.wav | sound_2_16.wav | sound_2_20.wav |