Thanks for this package. I was wondering about the L2 norm being applied in speech_embedder_net.py line 32. It looks like the paper describes an L2 norm being calculated per embedding and then dividing the embedding by that value. However, I believe the code as written calculates the norm over the entire matrix. Is this intentional? That is, I would have thought line 32 should be: x = x / torch.norm(x, dim=1).unsqueeze(1). This calculates the norm per embedding.
Thanks for this package. I was wondering about the L2 norm being applied in speech_embedder_net.py line 32. It looks like the paper describes an L2 norm being calculated per embedding and then dividing the embedding by that value. However, I believe the code as written calculates the norm over the entire matrix. Is this intentional? That is, I would have thought line 32 should be:
x = x / torch.norm(x, dim=1).unsqueeze(1)
. This calculates the norm per embedding.