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Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation

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您好,我今天查看Uformer模型的参数量,发现只有400多万个参数,占用17MB的memory,这比我预想的要低很多,请问就是这么低吗?

1. calloss_magmse全带幅度谱loss最后是除以batch和频点维度,而分段幅度谱loss(calloss_magmse_subband)除的是batch和帧数,因为output_mag.shape[2]应该是T维度; 2. 另外请问为什么不在T维度求平均呢?一般使用F.l1_loss的reduction直接用Mean就会在batch和F和T求平均,这样是对效果有啥影响吗?

作者您好,昨天的问题不知道怎么就不见了。我邮箱收到了您的回复,训练loss是正常的。我现在依然在查找问题的根源所在。如果您能把test 模型的源码公开,这对我解决问题会有很大的帮助。有一点需要咨询一下您,计算loss时为什么是返回三个值?期望您的回复。 昨天的问题是:测试模型时输出为静音。

Is there any script to run and use for training? Can you update the training script?

Thanks your creative work. But I encountered a problem in the process of reproduction, and I suspect it was a version problem.The questions are as follows: File "D:\DLSPEECHENHANCEMENT\Uformer\Uformer-main\uformer\conv2d_cplx.py", line 37,...

Hi, I am confused whether the Uformer works in real time. Best Regards!

The paper is one of the best out there, congrats! I am trying to run `uformer.py` but I get the following error: `RuntimeError: Given normalized_shape=[12], expected input with shape [*,...

Hi felix, Thanks for sharing this project, great work! I encountered some issues when running your code directly by: python uformer.py I have tried torch version from 1.8.1 to 1.10....