readme: ---
  tags:
  - audio-to-audio
  - asteroid
  - audio
  - audio-source-separation
  datasets:
  - wham
  - sep_clean
  license: cc-by-sa-4.0
  ---
  
  ## 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](https://github.com/asteroid-team/asteroid). It was trained on the sep_clean task of the WHAM! dataset.
  
  
  ### Demo: How to use in Asteroid
  
  ```python
  # 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
  
  ```BibTex
  @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:
  
  ```bibtex
  @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}
  }
  ```