Edit model card

Asteroid model mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean

♻️ Imported from https://zenodo.org/record/3903795#.X8pMBRNKjUI

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

Demo: How to use in Asteroid

# coming soon

Training config

  • data:
    • mode: min
    • nondefault_nsrc: None
    • sample_rate: 8000
    • segment: 2.0
    • task: sep_clean
    • train_dir: data/wav8k/min/tr
    • valid_dir: data/wav8k/min/cv
  • filterbank:
    • kernel_size: 16
    • n_filters: 64
    • stride: 8
  • main_args:
    • exp_dir: exp/train_dprnn_ks16/
    • help: None
  • masknet:
    • bidirectional: True
    • bn_chan: 128
    • chunk_size: 100
    • dropout: 0
    • hid_size: 128
    • hop_size: 50
    • in_chan: 64
    • mask_act: sigmoid
    • n_repeats: 6
    • n_src: 2
    • out_chan: 64
  • optim:
    • lr: 0.001
    • optimizer: adam
    • weight_decay: 1e-05
  • positional arguments:
  • training:
    • batch_size: 6
    • early_stop: True
    • epochs: 200
    • gradient_clipping: 5
    • half_lr: True
    • num_workers: 6

Results

  • si_sdr: 18.227683982688003
  • si_sdr_imp: 18.22883576588251
  • sdr: 18.617789605060587
  • sdr_imp: 18.466745426438173
  • sir: 29.22773720052717
  • sir_imp: 29.07669302190474
  • sar: 19.116352171914485
  • sar_imp: -130.06009796503054
  • stoi: 0.9722025377865715
  • stoi_imp: 0.23415680987800583

Citing Asteroid

@inproceedings{Pariente2020Asteroid,
    title={Asteroid: the {PyTorch}-based audio source separation toolkit for researchers},
    author={Manuel Pariente and Samuele Cornell and Joris Cosentino and Sunit Sivasankaran and
            Efthymios Tzinis and Jens Heitkaemper and Michel Olvera and Fabian-Robert Stöter and
            Mathieu Hu and Juan M. Martín-Doñas and David Ditter and Ariel Frank and Antoine Deleforge
            and Emmanuel Vincent},
    year={2020},
    booktitle={Proc. Interspeech},
}

Or on arXiv:

@misc{pariente2020asteroid,
      title={Asteroid: the PyTorch-based audio source separation toolkit for researchers}, 
      author={Manuel Pariente and Samuele Cornell and Joris Cosentino and Sunit Sivasankaran and Efthymios Tzinis and Jens Heitkaemper and Michel Olvera and Fabian-Robert Stöter and Mathieu Hu and Juan M. Martín-Doñas and David Ditter and Ariel Frank and Antoine Deleforge and Emmanuel Vincent},
      year={2020},
      eprint={2005.04132},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
Downloads last month
60
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Space using julien-c/DPRNNTasNet-ks16_WHAM_sepclean 1