HI Luc… First let me tell you that if the internet is a good place, it’s just because of people like you… so thank you.
Actually, I have a dataset as follows:
x_train:  of size (3082092, 13) (3082092 audio frames, each frame contains 13 features)
y_train: a (3082092, 1) , where each item is a label for the respective audio frame.
The classes = 50 (the number of different possible labels)
The goal is to do a classification for each frame. So for each single frame, there should be 50 probabilities and the correct label would be the highest probability.
That said, the input should be of batch_size  = 7, ie: 7 frames each time. each frame has 13 features.
Also the kernel_size = 7, and that’s why the next layer has a kernel_size = 1.