CS231n 3 gives a good overview how convolutions are used for image data. Even though you are dealing with Conv1d, the arithmetic is the same and you are just using a temporal dimension instead of the two spatial ones (height and width).
Basically, each kernel has a channel dimension, which is (in the vanilla setup) equal to the number of input channels. out_channels would correspond to the number of filters in the conv layer.