TaoRuijie
Vox1_E and Vox1_H
请check这里https://github.com/TaoRuijie/ECAPA-TDNN/issues/26#issuecomment-1060401825
def as_norm(score, embedding_1, embedding_2, cohort_feats, topk): score_1 = torch.matmul(cohort_feats, embedding_1.T)[:,0] score_1 = torch.topk(score_1, topk, dim = 0)[0] mean_1 = torch.mean(score_1, dim = 0) std_1 = torch.std(score_1, dim = 0) score_2 = torch.matmul(cohort_feats, embedding_2.T)[:,0] score_2 = torch.topk(score_2, topk, dim = 0)[0] mean_2 = torch.mean(score_2, dim = 0) std_2 = torch.std(score_2, dim = 0)
score = 0.5 * (score - mean_1) / std_1 + 0.5 * (score - mean_2) / std_2
cohort_feats is the extracted embedding of the training set, with the shape (N, 192), N is the number of training data
我看源代码中每一个样本是有两个embedding的，维度分别为（1，192）和（5，192），as_norm中只有一个，请问代表的是哪一个呢？是否考虑在as_norm中每个样本也加入两个embedding做规整然后求均值呢？