I see. One way to make sense of issues with a forward pass is to only take one individual piece at a time and confirm that your sample is compatible. In this case, the first convolution layer is a problem, if you were to use (randomly chosen kernel_size)

conv = nn.Conv1d(1, 80, kernel_size=3, stride=5)

Your sample would then be able to be passed through that single convolution layer. I’ll leave the parsing of the subsequent layers up to you 
Take a look at the docs to check/confirm that you are using items correctly: Conv1d — PyTorch 1.10.0 documentation