Comments (5)
The shape of all inputs is width * num_of_tokens, where widths are different for clean, noisy, and target.
Clean input and target are quantized. Noisy input is raw audio samples.
You need to create data matrix with appropriate shape and alignment. I used HDF5 just because my data matrix is created using Matlab.
I should mention all of these in Readme later.
from wavenet-enhancement.
First of all thanks for replay. have to clarify the above comments,
what you meant by the 'num_of_tokens', is that the output space size (ie 256)? or simply the strength of my data set .
from wavenet-enhancement.
num_of_tokens is the size of the data set, similar to "batch_size".
channel is the output space size you are referring to.
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could you tell me how to create the input data?
from wavenet-enhancement.
The clean input is the quantized audio ranging from 0 to 255.
The noisy input is the corresponding noisy version in raw audio samples.
from wavenet-enhancement.
Related Issues (15)
- Hi,
- model HOT 2
- Pre-trained model please
- tower是什么意思啊 HOT 1
- How much time does it take to train this model? HOT 3
- Can i run it on a single GPU?
- Key not found in checkpoint HOT 1
- input data
- Can you teach me how to create input data? HOT 3
- Output data slice
- Which pretrained model to liklihood training HOT 2
- Command to use trained model to test on noisy speech HOT 2
- Should I expect this model generalize well on unseen speaker? HOT 1
- the input to "bawn_pr_multi_gpu_train.py" HOT 6
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