This is an Audacity wrapper for the model, forked from the repository mpariente/ConvTasNet_WHAM_sepclean, This model was trained using the Asteroid library:

The following info was copied from mpariente/ConvTasNet_WHAM_sepclean:


This model was trained by Manuel Pariente using the wham/ConvTasNet recipe in Asteroid. It was trained on the sep_clean task of the WHAM! dataset.

Training config:

    n_src: 2
    mode: min
    nondefault_nsrc: None
    sample_rate: 8000
    segment: 3
    task: sep_clean
    train_dir: data/wav8k/min/tr/
    valid_dir: data/wav8k/min/cv/
    kernel_size: 16
    n_filters: 512
    stride: 8
    exp_dir: exp/wham
    gpus: -1
    help: None
    bn_chan: 128
    hid_chan: 512
    mask_act: relu
    n_blocks: 8
    n_repeats: 3
    n_src: 2
    skip_chan: 128
    lr: 0.001
    optimizer: adam
    weight_decay: 0.0
positional arguments:
    batch_size: 24
    early_stop: True
    epochs: 200
    half_lr: True
    num_workers: 4


si_sdr: 16.21326632846293
si_sdr_imp: 16.21441705664987
sdr: 16.615180021738933
sdr_imp: 16.464137807433435
sir: 26.860503975131923
sir_imp: 26.709461760826414
sar: 17.18312813480803
sar_imp: -131.99332048277296
stoi: 0.9619940905157323
stoi_imp: 0.2239480672473015

License notice:

This work "ConvTasNet_WHAM!_sepclean" is a derivative of CSR-I (WSJ0) Complete by LDC, used under LDC User Agreement for Non-Members (Research only). "ConvTasNet_WHAM!_sepclean" is licensed under Attribution-ShareAlike 3.0 Unported by Manuel Pariente.

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