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@@ -18,7 +18,7 @@ metrics:
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  ---
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  # SepFormer trained on WHAM!
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- This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAMR!](http://wham.whisper.ai/) dataset, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance is 13.3 dB SI-SNRi on the test set of WHAMR! dataset.
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  | Release | Test-Set SI-SNRi | Test-Set SDRi |
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  |:-------------:|:--------------:|:--------------:|
@@ -61,7 +61,7 @@ torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000)
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  year = {2021},
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  publisher = {GitHub},
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  journal = {GitHub repository},
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- howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
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  }
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  ```
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  ---
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  # SepFormer trained on WHAM!
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+ This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAMR!](http://wham.whisper.ai/) dataset, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance is 13.7 dB SI-SNRi on the test set of WHAMR! dataset.
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  | Release | Test-Set SI-SNRi | Test-Set SDRi |
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  |:-------------:|:--------------:|:--------------:|
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  year = {2021},
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  publisher = {GitHub},
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  journal = {GitHub repository},
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+ howpublished = {\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
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  }
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  ```
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