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:

yaml

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.

groadabike/ConvTasNet_DAMP-VSEP_enhboth

Asteroid model groadabike/ConvTasNet_DAMP-VSEP_enhboth

Description:

Training config:

Results:

License notice:

groadabike/
ConvTasNet_DAMP-VSEP_enhboth

Asteroid model `groadabike/ConvTasNet_DAMP-VSEP_enhboth`