Comments (2)
@HiroshiKawata
Current model actually don't learn anything because it's initialized totally randomly.
The training process is to repeatly optimize the source sound piece so that it's network output features meet our requirements, therefore the model parameter won't be updated. So there is not use to save model parameters.
However, your question reminds me of a recently paper(github implementation) which shows that image style transfer task can be learned by a linear model. So maybe it's reasonable to train a model like that.
from randomcnn-voice-transfer.
I see. Thanks for the explanation and the paper, @mazzzystar ! 😃
from randomcnn-voice-transfer.
Related Issues (20)
- bad result i git!!
- Some unclear details. HOT 1
- can't optimize a non-leaf Tensor HOT 1
- required os and python versions? HOT 3
- Input output??? HOT 1
- Question: difference between this repo and others? HOT 1
- Bad very long time to train compare the original project DmitryUlyanov
- no voice
- RuntimeError: CUDA error: unspecified launch failure HOT 1
- AttributeError: module 'librosa' has no attribute 'output' HOT 1
- Any updates to this? HOT 2
- Question
- when I run train, some problems came out: HOT 9
- How to Train this repo, can you please eloborate ? HOT 4
- Installation Environment HOT 2
- some problems HOT 17
- import torch error HOT 7
- RuntimeError: CUDA out of memory HOT 1
- Phase restoration algorithm HOT 2
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from randomcnn-voice-transfer.