master64 same model as reported in the paper? about denoiser HOT 6 CLOSED

Ohhh sorry about that...:)
I'll upload the non-causal version soon. The non-causal version was trained on VCTK only, it might be a good idea to train it on a bigger and more challenging set.

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The numbers you get are fine. The master model was trained on both DNS and Valentini datasets. Its results are not reported in the manuscript. We provide this model so people can use a better performing model for downstream tasks / clean noisy files / etc.
Regarding non-causal training, you can pass demucs.causal=False to train a non-causal model.

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Thanks for your response! Appreciate it! On my second point, I was more interested in getting access to a pretrained non-causal model. I was curious as to how much better could that model be. The current master64 model is great, but still has a few artifacts that I can observe. I was wondering it the non-causal model gets rid of that?

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where can I find the pretrained models?

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Hi @zhangxingtao ,
You can add --master64 to your evaluation script and it will automatically download the pre-trained model.
You can do the same with --dns48 and --dns64.

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If you want to fine tune from a pre-train model, please see this issue: #7

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