o ei @s4dZddlZ d dejdejdedejfdd ZdS) z&Distance metrics and related functionsN:0yE>abepsreturncCs||ks Jd|dksJd|jdd|jddks(Jdtjj|ddd}tjj|ddd}|tj||d }|tj||d }||ddS) aComputes a matrix evaluating all pairwise cosine similarities. The cosine similarity can otherwise be determined with :class:`torch.nn.CosineSimilarity`. Arguments --------- a : torch.Tensor Tensor of shape `[..., X, dim]` where `dim` is the dimension where the cosine similarity will be computed and `X` is any value `>= 0`. b : torch.Tensor Tensor of shape `[..., Y, dim]`, where other dimensions are otherwise identical to `a`'s and `Y` is any value `>= 0`. eps : float Epsilon value for numerical stability, in order to avoid a division by zero. Does not significantly affect results. Returns ------- torch.Tensor Tensor of shape `[..., X, Y]` living on the same device and dtype as the input tensors. e.g. ignoring first dimensions `out[3, 0]` would be the cosine similarity of `a[3]` and `b[0]`. zInputs must be of the same dimz)Expected at least 2 dims [X, cos_sim_dim]Nz(Input shape must match until last 2 dims)dim)min)r shapetorchlinalg vector_norm unsqueezeclamp transpose)rrra_normb_norm a_normalized b_normalizedrY/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/speechbrain/utils/distances.pycosine_similarity_matrixsr)r)__doc__r Tensorfloatrrrrrs