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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- Source Separation |
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- Speech Separation |
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- Audio Source Separation |
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- WHAM! |
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- SepFormer |
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- Transformer |
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license: "apache-2.0" |
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datasets: |
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- WHAM! |
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metrics: |
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- SI-SNRi |
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- SDRi |
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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 [WHAM!](http://wham.whisper.ai/) dataset, which is basically a version of WSJ0-Mix dataset with environmental noise. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance is 16.3 dB SI-SNRi on the test set of WHAM! dataset. |
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| Release | Test-Set SI-SNRi | Test-Set SDRi | |
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|:-------------:|:--------------:|:--------------:| |
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| 09-03-21 | 16.3 dB | 16.7 dB | |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). |
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### Perform source separation on your own audio file |
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```python |
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from speechbrain.pretrained import SepformerSeparation as separator |
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import torchaudio |
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model = separator.from_hparams(source="speechbrain/sepformer-wham", savedir='pretrained_models/sepformer-wham') |
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# for custom file, change path |
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est_sources = model.separate_file(path='speechbrain/sepformer-wsj02mix/test_mixture.wav') |
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torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000) |
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torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000) |
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``` |
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#### Referencing SpeechBrain |
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``` |
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@misc{SB2021, |
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author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, |
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title = {SpeechBrain}, |
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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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#### Referencing SepFormer |
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``` |
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@inproceedings{subakan2021attention, |
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title={Attention is All You Need in Speech Separation}, |
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author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong}, |
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year={2021}, |
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booktitle={ICASSP 2021} |
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} |
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``` |
