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View Code? Open in Web Editor NEWunofficial PyTorch implementation of 《REAL-TIME DENOISING AND DEREVERBERATION WTIH TINY RECURRENT U-NET》
unofficial PyTorch implementation of 《REAL-TIME DENOISING AND DEREVERBERATION WTIH TINY RECURRENT U-NET》
Hi
As per the paper, 4 features must be concatenated as input to TRUNET,
so the input will become (Batchsize, 4 features, No.of frames in STFT, No.of STFT bins) , so it is a 4 dimesional one
But in the sample code you are showing input as 3 dimension (1,4,257), since first layer is conv1d
I'm confused whether the input to TRUNET is 3 dimension or 4 dimension ?
Regards
Yugesh
我最近在使用 TRUNet 进行训练,和文中不同,为了避免相位估计的问题,我直接用复数谱的实部和虚部拼接作为输入特征。也即是输入特征维度为(T, 2, F),模型输出对实部和虚部的mask估计,虽然最终的性能正常,但是在训练过程中,train loss 下降很平稳, 验证 loss 震荡相当严重,增大了batch或者调小了学习率后也没有改善,不知道你在训练的时候遇到过这种情况吗?
The shape of the output is [1, 5, 257], 1 for the batch, 257 for the frequency. Could you please tell me what 5 means? Thank you so much.
the model input shape is 【1,4,257】,while the output shape is 【1,5,257】,
It will be good for the researchers / students if you share the working version of training script
T-GRU和F-GRU的输入shape好像是一样的,所以这里怎么提现这个T-GRU?
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