Speech Enhancement Generative Adversarial Network in PyTorch

Execute training with default parameters giving latest best results in denoising and dewhispering:

python train.py --save_path ckpt_segan+ --batch_size 300 \
		--clean_trainset data/clean_trainset \
		--noisy_trainset data/noisy_trainset \
		--cache_dir data/cache

Read run_segan+_train.sh for more guidance

Clean files by specifying the generator weights checkpoint, its config file from training and appropriate paths for input and output files (Use soundfile wav writer backend (recommended) specifying the --soundfile flag):

python clean.py --g_pretrained_ckpt ckpt_segan+/<weights_ckpt_for_G> \
		--cfg_file ckpt_segan+/train.opts --synthesis_path enhanced_results \
		--test_files data/noisy_testset --soundfile

Read run_segan+_clean.sh for more guidance

Disclaimer:

• Multi-GPU is not supported yet in this framework.
• Virtual Batch Norm is not included, and similar results to those of original paper can be obtained with regular BatchNorm in D (ONLY D).