mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean

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: 16
  • n_filters: 64
  • stride: 8
• main_args:
  • exp_dir: exp/train_dprnn_ks16/
  • help: None
• masknet:
  • bidirectional: True
  • bn_chan: 128
  • chunk_size: 100
  • dropout: 0
  • hid_size: 128
  • hop_size: 50
  • 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: 6
  • early_stop: True
  • epochs: 200
  • gradient_clipping: 5
  • half_lr: True
  • num_workers: 6

Results:

• si_sdr: 18.227683982688003
• si_sdr_imp: 18.22883576588251
• sdr: 18.617789605060587
• sdr_imp: 18.466745426438173
• sir: 29.22773720052717
• sir_imp: 29.07669302190474
• sar: 19.116352171914485
• sar_imp: -130.06009796503054
• stoi: 0.9722025377865715
• stoi_imp: 0.23415680987800583

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.