Comments (1)
这是FullSubNet原来的代码里面用来训练加速的一个方法,在训练的时候,target和模型内部都会dropband,而在inference的时候就是正常的inference(因为每个subband units相当于是被模型视作了batch,不影响模型的输入输出维度)
from fullsubnet-plus.
Related Issues (20)
- small code fix needed in clipping detection HOT 3
- Model causality HOT 2
- Query regarding Causality of Model HOT 7
- About the efficiency of the MulCA module HOT 5
- Control the strength of Enhancement HOT 3
- Question about the output of Quick Start. HOT 5
- soundfile.LibsndfileError: Error opening 'xx/xx/xx.wav': File contains data in an unknown format. HOT 8
- How do I create my own dataset? HOT 3
- 有关流式推理的探索。 HOT 3
- OSError: [Errno 22] Invalid argument: '.../FullSubNet-plus/result/output/2022-11-11 01:25:28.toml' HOT 5
- When I'm training the test loss is zero HOT 1
- Inference is getting Killed automatically. HOT 3
- Error occurred while training the FullSubNet-Plus model HOT 4
- pypesq install failure HOT 2
- Will the use of AdaptiveAvgPool1d in MulCA lead to loss of causality? HOT 1
- train.toml出错 HOT 6
- torch.istft requires a complex-valued input tensor matching the output from stft with return_complex=True HOT 2
- Loss function clarification
- 关于rir数据
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from fullsubnet-plus.