This is an Audacity wrapper for the model, forked from the repository mpariente/ConvTasNet_WHAM_sepclean, This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied from
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.
data: 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/ filterbank: kernel_size: 16 n_filters: 512 stride: 8 main_args: exp_dir: exp/wham gpus: -1 help: None masknet: bn_chan: 128 hid_chan: 512 mask_act: relu n_blocks: 8 n_repeats: 3 n_src: 2 skip_chan: 128 optim: lr: 0.001 optimizer: adam weight_decay: 0.0 positional arguments: training: 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
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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