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README.md
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
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<br/><br/>
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# SepFormer trained on
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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 with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 16k. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset.
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year = {2021},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {
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}
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```
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
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<br/><br/>
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# SepFormer trained on WHAMR! (16k sampling frequency)
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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 with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 16k. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset.
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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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