Yes, it should give the same results.
Here is a small script to reproduce this:
x = torch.randn(1024,9,128)

conv1d = nn.Conv1d(9, 1, 2)
conv2d = nn.Conv2d(9, 1, (2, 1))
with torch.no_grad():
    conv2d.weight.copy_(conv1d.weight.unsqueeze(3))
    conv2d.bias.copy_(conv1d.bias)

output1d = conv1d(x)
output2d = conv2d(x.unsqueeze(3))

print((output1d == output2d.squeeze(3)).all())

I would guess it’s convenient to use nn.Conv1d, if your data has only one temporal dimension, instead of having to specify two dimensions for the kernel_size, padding and stride explicitly.
Also, I’m not sure about the underlying implementations (especially how cuDNN handles 1D data), which might result in speed differences.