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A PyTorch implementation of dual-path RNNs (DPRNNs) based speech separation described in "Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation".

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您好,我想问下您运行完100个epoch需要花多少时间呢?因为batch_size有点小,在我这里运行时间有点长,是否可以设置的更大一点呢?会对实验结果有影响吗?

hi,thanks for sharing the code,could you please show us an example mixwav ?

Hello, thanks for sharing useful code. I want to implement "DUAL-PATH RNN: EFFICIENT LONG SEQUENCE MODELING FOR TIME-DOMAIN SINGLE-CHANNEL SPEECH SEPARATION" paper. So I decided to use **DPRNN_base** model instead...

in solver.py ``` def _run_one_epoch(self, epoch, cross_valid=False): start = time.time() total_loss = 0 data_loader = self.tr_loader if not cross_valid else self.cv_loader print('data_loader.len {}'.format(len(data_loader))) for i, (data) in enumerate(data_loader): padded_mixture_, mixture_lengths_,...

Hello, thanks for your efforts in this experiment. I am reproducing your experiment. But I found there is an error about cuda memory. Could I know GPUs memory you used...

It seems that you add a beamforming class in this model ,does this mean we can use multi channel datasets?

hi, l want to know your datsetset such as WSJ0-mix how to get it,thank you

Hello,thanks for your help on dprnn.I try to use the code but I have meet a problem when I use the command:.CUDA_VISIBLE_DEVICES=0 python train.py --train_dir data/tr --valid_dir data/cv --sample_rate 8000...