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--- |
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tags: |
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- asteroid |
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- audio |
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- ConvTasNet |
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- audio-source-separation |
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datasets: |
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- wham |
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- sep_clean |
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license: cc-by-sa-3.0 |
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inference: false |
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--- |
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## Asteroid model `mpariente/ConvTasNet_WHAM_sepclean` |
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Imported from [Zenodo](https://zenodo.org/record/3862942) |
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### Description: |
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This model was trained by Manuel Pariente |
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using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid). |
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It was trained on the `sep_clean` task of the WHAM! dataset. |
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### Training config: |
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```yaml |
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data: |
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channels: 1 |
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n_src: 2 |
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root_path: data |
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sample_rate: 16000 |
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samples_per_track: 10 |
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segment: 3.0 |
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task: enh_both |
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filterbank: |
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kernel_size: 20 |
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n_filters: 256 |
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stride: 10 |
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main_args: |
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exp_dir: exp/train_convtasnet |
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help: None |
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masknet: |
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bn_chan: 256 |
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conv_kernel_size: 3 |
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hid_chan: 512 |
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mask_act: relu |
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n_blocks: 8 |
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n_repeats: 4 |
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n_src: 2 |
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norm_type: gLN |
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skip_chan: 256 |
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optim: |
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lr: 0.0003 |
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optimizer: adam |
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weight_decay: 0.0 |
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positional arguments: |
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training: |
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batch_size: 12 |
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early_stop: True |
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epochs: 50 |
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half_lr: True |
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num_workers: 12 |
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``` |
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### Results: |
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```yaml |
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si_sdr: 14.018196157142519 |
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si_sdr_imp: 14.017103133809577 |
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sdr: 14.498517291333885 |
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sdr_imp: 14.463389151567865 |
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sir: 24.149634529133372 |
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sir_imp: 24.11450638936735 |
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sar: 15.338597389045935 |
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sar_imp: -137.30634122401517 |
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stoi: 0.7639416744417206 |
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stoi_imp: 0.1843383526963759 |
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``` |
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### License notice: |
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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. |
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