I just see it by expertise   but maths can be used too!
Basically when you apply a convolution the resulting signal 's size is gonna be reduced unless you pad the input. (You can google about it).
In this case the size of the output is 40945. Since it’s more or less the same size as the input, we can know the stride=1. For stride 2 the signal size would be halved.
If you check the docs, you have the mathematical expression to calculate the size:
https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html#torch.nn.Conv1d 1

imagen938×191 11.1 KB

In this case we can see the following:

Input signal’s size ~= output’s size → stride=1
Dilation=1 as they say nothing about it. They could use diletion but, if that were the case, they could just downsample the signal beforehand.
Padding=0 as the size of the input is similar to the size of the output. Still they could pad and use a bigger kernel size.
Then if we apply the formula we have 40960 - 2*(16-1)+1-1= 490945.

So in short 40945 describes the number of elements in the time series after you apply the convolution.
32 describes the amount of filters used.