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 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

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.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

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