Description: This model was trained by Manuel Pariente using the wham/DPRNN recipe in Asteroid. It was trained on the sep_clean task of the WHAM! dataset. Training config: data: mode: min nondefault_nsrc: None sample_rate: 8000 segment: 2.0 task: sep_clean train_dir: data/wav8k/min/tr valid_dir: data/wav8k/min/cv filterbank: kernel_size: 2 n_filters: 64 stride: 1 main_args: exp_dir: exp/train_dprnn_new/ gpus: -1 help: None masknet: bidirectional: True bn_chan: 128 chunk_size: 250 dropout: 0 hid_size: 128 hop_size: 125 in_chan: 64 mask_act: sigmoid n_repeats: 6 n_src: 2 out_chan: 64 optim: lr: 0.001 optimizer: adam weight_decay: 1e-05 positional arguments: training: batch_size: 3 early_stop: True epochs: 200 gradient_clipping: 5 half_lr: True num_workers: 8 Results: si_sdr: 19.316743490695334 si_sdr_imp: 19.317895273889842 sdr: 19.68085347190952 sdr_imp: 19.5298092932871 sir: 30.362213998701232 sir_imp: 30.21116982007881 sar: 20.15553251343315 sar_imp: -129.02091762351188 stoi: 0.97772664309074 stoi_imp: 0.23968091518217424 License notice: This work "DPRNNTasNet_WHAM!_sepclean" is a derivative of CSR-I (WSJ0) Complete by LDC, used under LDC User Agreement for Non-Members (Research only). "DPRNNTasNet_WHAM!_sepclean" is licensed under Attribution-ShareAlike 3.0 Unported by Manuel Pariente.