pre-trained checkpoints

We retrained the model after fixing the bug in loss funtion and upload the pre-trained model again, trained on VB with 0.5 as the dropout ratio.

Using this pre-trained checkpoint, we got:

csig:3.8264 cbak:3.2791 covl:3.1965 pesq:2.5878 ssnr:9.3684 stoi:0.9336 on VB

csig:2.7520 cbak:2.1276 covl:2.0501 pesq:1.4693 ssnr:1.8087 stoi:0.8304 on CHIME-4

pre-trained checkpoints

We upload the pre-trained model, trained on VB with 0.5 as the dropout ratio.

We got:

csig:3.8357 cbak:3.2350 covl:3.1840 pesq:2.5430 ssnr:8.9398 stoi:0.9335 on VB

csig:2.8673 cbak:2.1805 covl:2.1647 pesq:1.5709 ssnr:1.6121 stoi:0.8673 on CHIME-4

Weigths-dis is the checkpoint of '1step dis' in the experimental part of the paper, trained on VB.

We got:

csig:3.6228 cbak:3.2605 covl:3.0523 pesq:2.4946 ssnr:9.7642 stoi:0.9327 on VB

csig:2.2069 cbak:1.9519 covl:1.6281 pesq:1.2173 ssnr:1.9884 stoi:0.7230 on CHIME-4