mpariente/DPRNNTasNet_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: 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.