Instructions to use BluePrintTs/NoiseManagementSystem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Asteroid
How to use BluePrintTs/NoiseManagementSystem with Asteroid:
from asteroid.models import BaseModel model = BaseModel.from_pretrained("BluePrintTs/NoiseManagementSystem") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - asteroid | |
| - audio | |
| - ConvTasNet | |
| - audio-to-audio | |
| datasets: | |
| - Libri2Mix | |
| - sep_noisy | |
| license: cc-by-sa-4.0 | |
| ` | |
| [](https://zenodo.org/record/3874420#.X9I6NcLjJH4) | |
| Training config: | |
| ```yml | |
| data: | |
| n_src: 2 | |
| sample_rate: 8000 | |
| segment: 3 | |
| task: sep_noisy | |
| train_dir: data/wav8k/min/train-360 | |
| valid_dir: data/wav8k/min/dev | |
| filterbank: | |
| kernel_size: 16 | |
| n_filters: 512 | |
| stride: 8 | |
| masknet: | |
| bn_chan: 128 | |
| hid_chan: 512 | |
| mask_act: relu | |
| n_blocks: 8 | |
| n_repeats: 3 | |
| skip_chan: 128 | |
| optim: | |
| lr: 0.001 | |
| optimizer: adam | |
| weight_decay: 0.0 | |
| training: | |
| batch_size: 24 | |
| early_stop: True | |
| epochs: 200 | |
| half_lr: True | |
| num_workers: 4 | |
| ``` | |
| Results: | |
| On Libri2Mix min test set : | |
| ```yml | |
| si_sdr: 9.944424856077259 | |
| si_sdr_imp: 11.939395359731192 | |
| sdr: 10.701526190782072 | |
| sdr_imp: 12.481757547845662 | |
| sir: 22.633644975545575 | |
| sir_imp: 22.45666740833025 | |
| sar: 11.131644100944868 | |
| sar_imp: 4.248489589311784 | |
| stoi: 0.852048619949357 | |
| stoi_imp: 0.2071994899565506 | |
| ``` | |