RuntimeError: Error(s) in loading state_dict for DTDNN: Missing key(s) in state_dict: "xvector.tdnn.linear.bias", "xvector.dense.linear.bias". size mismatch for xvector.tdnn.linear.weight: copying a param with shape torch.Size([128, 30, 5]) from checkpoint, the shape in current model is torch.Size([128, 150]). size mismatch for xvector.block1.tdnnd1.linear1.weight: copying a param with shape torch.Size([128, 128, 1]) from checkpoint, the shape in current model is torch.Size([128, 128]). size mismatch for xvector.block1.tdnnd1.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd2.linear1.weight: copying a param with shape torch.Size([128, 192, 1]) from checkpoint, the shape in current model is torch.Size([128, 192]). size mismatch for xvector.block1.tdnnd2.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd3.linear1.weight: copying a param with shape torch.Size([128, 256, 1]) from checkpoint, the shape in current model is torch.Size([128, 256]). size mismatch for xvector.block1.tdnnd3.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd4.linear1.weight: copying a param with shape torch.Size([128, 320, 1]) from checkpoint, the shape in current model is torch.Size([128, 320]). size mismatch for xvector.block1.tdnnd4.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd5.linear1.weight: copying a param with shape torch.Size([128, 384, 1]) from checkpoint, the shape in current model is torch.Size([128, 384]). size mismatch for xvector.block1.tdnnd5.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd6.linear1.weight: copying a param with shape torch.Size([128, 448, 1]) from checkpoint, the shape in current model is torch.Size([128, 448]). size mismatch for xvector.block1.tdnnd6.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.transit1.linear.weight: copying a param with shape torch.Size([256, 512, 1]) from checkpoint, the shape in current model is torch.Size([256, 512]). size mismatch for xvector.block2.tdnnd1.linear1.weight: copying a param with shape torch.Size([128, 256, 1]) from checkpoint, the shape in current model is torch.Size([128, 256]). size mismatch for xvector.block2.tdnnd1.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd2.linear1.weight: copying a param with shape torch.Size([128, 320, 1]) from checkpoint, the shape in current model is torch.Size([128, 320]). size mismatch for xvector.block2.tdnnd2.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd3.linear1.weight: copying a param with shape torch.Size([128, 384, 1]) from checkpoint, the shape in current model is torch.Size([128, 384]). size mismatch for xvector.block2.tdnnd3.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd4.linear1.weight: copying a param with shape torch.Size([128, 448, 1]) from checkpoint, the shape in current model is torch.Size([128, 448]). size mismatch for xvector.block2.tdnnd4.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd5.linear1.weight: copying a param with shape torch.Size([128, 512, 1]) from checkpoint, the shape in current model is torch.Size([128, 512]). size mismatch for xvector.block2.tdnnd5.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd6.linear1.weight: copying a param with shape torch.Size([128, 576, 1]) from checkpoint, the shape in current model is torch.Size([128, 576]). size mismatch for xvector.block2.tdnnd6.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd7.linear1.weight: copying a param with shape torch.Size([128, 640, 1]) from checkpoint, the shape in current model is torch.Size([128, 640]). size mismatch for xvector.block2.tdnnd7.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd8.linear1.weight: copying a param with shape torch.Size([128, 704, 1]) from checkpoint, the shape in current model is torch.Size([128, 704]). size mismatch for xvector.block2.tdnnd8.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd9.linear1.weight: copying a param with shape torch.Size([128, 768, 1]) from checkpoint, the shape in current model is torch.Size([128, 768]). size mismatch for xvector.block2.tdnnd9.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd10.linear1.weight: copying a param with shape torch.Size([128, 832, 1]) from checkpoint, the shape in current model is torch.Size([128, 832]). size mismatch for xvector.block2.tdnnd10.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd11.linear1.weight: copying a param with shape torch.Size([128, 896, 1]) from checkpoint, the shape in current model is torch.Size([128, 896]). size mismatch for xvector.block2.tdnnd11.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd12.linear1.weight: copying a param with shape torch.Size([128, 960, 1]) from checkpoint, the shape in current model is torch.Size([128, 960]). size mismatch for xvector.block2.tdnnd12.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.transit2.linear.weight: copying a param with shape torch.Size([512, 1024, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024]). size mismatch for xvector.dense.linear.weight: copying a param with shape torch.Size([512, 1024, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024]).

您好： 我想教下您的论文中，实验的实现细节： 1.实验数据：我看很多其他论文都是使用voxceleb2 dev 5994说话人作为训练集（或者voxceleb dev+voxceleb2 dev，1211+5994说话人），您有只在这部分说话人上的实验结果吗？方便透露下嘛？

2.PLDA和Cosine Similarity：您这里实验比较这两个的EER在TDNN中是提取的是倒数第二层（分类器前一层）还是第三层(xvector)的输出啊？因为我在论文中又看到，这两个不同层embedding对不同方法性能有差异,倒数第二层的cosine方法可能会更好一些。

Thanks！🙏

hello, yuyq96, Thank you so much for the great work you've shared. I learned that D-TDNNSS mini-batch setting 128 from D-TDNN paper. But this model is too large to train on single gpu. Could you tell me how you train it? Using nn.Parallel or DDP? Looking forward to you reply

May I ask you the numerical difference between kaldifeat by kaldi implementation and kaldifeat by your python implementation? I have compared the two computed features, and I find it has some difference. I wonder that the experiment results showed in D-TDNN master and D-TDNN-kaldifeat branch is absolutely the same.

Thanks~

您好，我觉得您的工作-DTDNN，在参数比较少的情况下获得了较ETDNN，FTDNN更好的结果，我认为这非常有意义。但是我对论文的实验存在两处疑惑： 1、论文中Table5中，基于softmax训练的D-TDNN模型Cosine的结果好于PLDA，在上面的TDNN，ETDNN，FTDNN的结果不一致（均是PLDA好于Cosine），请问这是什么原因导致的？ 2、对于null branch，能稍微解释一下吗？