| tags: | |
| - asteroid | |
| - audio | |
| - ConvTasNet | |
| - audio-to-audio | |
| datasets: | |
| - libri1mix | |
| - enh_single | |
| license: cc-by-sa-4.0 | |
| ## Asteroid model `mhu-coder/ConvTasNet_Libri1Mix_enhsingle` | |
| Imported from [Zenodo](https://zenodo.org/record/4301955#.X9cj98Jw0bY) | |
| ### Description: | |
| This model was trained by Mathieu Hu using the librimix/ConvTasNet recipe in | |
| [Asteroid](https://github.com/asteroid-team/asteroid). | |
| It was trained on the `enh_single` task of the Libri1Mix dataset. | |
| ### Training config: | |
| ```yaml | |
| data: | |
| n_src: 1 | |
| sample_rate: 16000 | |
| segment: 3 | |
| task: enh_single | |
| train_dir: data/wav16k/min/train-100 | |
| valid_dir: data/wav16k/min/dev | |
| filterbank: | |
| kernel_size: 16 | |
| n_filters: 512 | |
| stride: 8 | |
| main_args: | |
| exp_dir: exp/train_convtasnet_f34664b9 | |
| help: None | |
| masknet: | |
| bn_chan: 128 | |
| hid_chan: 512 | |
| mask_act: relu | |
| n_blocks: 8 | |
| n_repeats: 3 | |
| n_src: 1 | |
| skip_chan: 128 | |
| optim: | |
| lr: 0.001 | |
| optimizer: adam | |
| weight_decay: 0.0 | |
| positional arguments: | |
| training: | |
| batch_size: 2 | |
| early_stop: True | |
| epochs: 200 | |
| half_lr: True | |
| num_workers: 4 | |
| ``` | |
| ### Results: | |
| ```yaml | |
| si_sdr: 13.938355526049932 | |
| si_sdr_imp: 10.488574220190232 | |
| sdr: 14.567380104207393 | |
| sdr_imp: 11.064717304994337 | |
| sir: inf | |
| sir_imp: nan | |
| sar: 14.567380104207393 | |
| sar_imp: 11.064717304994337 | |
| stoi: 0.9201010933251715 | |
| stoi_imp: 0.1241812697846321 | |
| ``` | |
| ### License notice: | |
| This work "ConvTasNet_Libri1Mx_enhsingle" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A) | |
| by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for | |
| Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only). | |
| "ConvTasNet_Libri1Mix_enhsingle" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) | |
| by Mathieu Hu. | |