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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- audio-source-separation |
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- Source Separation |
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- Speech 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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- WHAMR! |
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metrics: |
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- SI-SNRi |
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- SDRi |
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pipeline: |
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- audio source separation |
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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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| Release | Test-Set SI-SNRi | Test-Set SDRi | |
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|:-------------:|:--------------:|:--------------:| |
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| 30-03-21 | 13.5 dB | 13.0 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-whamr16k", savedir='pretrained_models/sepformer-whamr16k') |
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# for custom file, change path |
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est_sources = model.separate_file(path='speechbrain/sepformer-whamr16k/test_mixture16k.wav') |
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torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 16000) |
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torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 16000) |
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``` |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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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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``` |
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#### About SpeechBrain |
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SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. |
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Website: https://speechbrain.github.io/ |
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GitHub: https://github.com/speechbrain/speechbrain |
