Replies: 1 comment 4 replies
-
I'm not sure, if it was related to the NaN issue it would just completely not work. And have you properly prepared the dataset? |
Beta Was this translation helpful? Give feedback.
4 replies
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
-
First of all I should say that I have most deep learning non-friendly GPU 1660 super, I know pytorch don't like this GPU and that it produces NaN when deals with fp16 (half). But I see you fixed it (#90) so that feature extraction uses fp32.
But what about training? Is it uses fp16 somewhere?
My problem is that no matter how many epochs I train model result voice is same as in base checkpoint f0. I tried train on my own dataset and crystal clear dataset from huggingface and both sounds exact as f0. Train config is mangio-crepe, 128 hop length, 3 filter radius, 1e-4 learning rate. After training stopped I refresh checkpoints list, select new checkpoint, click "copy to rvc models" and in rvc tab first unload, refresh and select model.
I tested Mangio-RVC-Fork, after little modification (use CPU for feature extraction) even after 10 epochs on both datasets result is noticeable, specially on dataset from huggingface.
Beta Was this translation helpful? Give feedback.
All reactions