Dprnntasnet Ks16 WHAM Sepclean

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

This is an audio source separation model trained on the WHAM! dataset using the Asteroid framework, which can effectively separate different audio sources.

🚀 Quick Start

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.

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

💻 Usage Examples

Basic Usage

# coming soon

📚 Documentation

Training config

Results

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}
}

📄 License

This model is released under the cc-by-sa-4.0 license.