groadabike/ConvTasNet_DAMP-VSEP_enhboth

Description:

This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.

Training config:

• data:
  • channels: 1
  • n_src: 2
  • root_path: data
  • sample_rate: 16000
  • samples_per_track: 10
  • segment: 3.0
  • task: enh_both
• filterbank:
  • kernel_size: 20
  • n_filters: 256
  • stride: 10
• main_args:
  • exp_dir: exp/train_convtasnet
  • help: None
• masknet:
  • bn_chan: 256
  • conv_kernel_size: 3
  • hid_chan: 512
  • mask_act: relu
  • n_blocks: 8
  • n_repeats: 4
  • n_src: 2
  • norm_type: gLN
  • skip_chan: 256
• optim:
  • lr: 0.0003
  • optimizer: adam
  • weight_decay: 0.0
• positional arguments:
• training:
  • batch_size: 12
  • early_stop: True
  • epochs: 50
  • half_lr: True
  • num_workers: 12

Results:

• si_sdr: 14.018196157142519
• si_sdr_imp: 14.017103133809577
• sdr: 14.498517291333885
• sdr_imp: 14.463389151567865
• sir: 24.149634529133372
• sir_imp: 24.11450638936735
• sar: 15.338597389045935
• sar_imp: -137.30634122401517
• stoi: 0.7639416744417206
• stoi_imp: 0.1843383526963759

License notice:

This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.