---
language: "en"
thumbnail:
tags:
- Source Separation
- Speech Separation
- Audio Source Separation
- WHAM!
- SepFormer
- Transformer 
license: "apache-2.0"
datasets:
- WHAM!
metrics:
- SI-SNRi
- SDRi

---

# SepFormer trained on WHAM!
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. 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.

| Release | Test-Set SI-SNRi | Test-Set SDRi |
|:-------------:|:--------------:|:--------------:|
| 09-03-21 | 16.3 dB | 16.7 dB |


## Install SpeechBrain

First of all, please install SpeechBrain with the following command:

```
pip install \\we hide ! SpeechBrain is still private :p
```

Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).

### Perform source separation on your own audio file

```python

from speechbrain.pretrained import separator
import torchaudio

model = separator.from_hparams(source="speechbrain/sepformer-wham")

mix, fs = torchaudio.load("yourspeechbrainpath/samples/audio_samples/test_mixture.wav")

est_sources = model.separate(mix)
est_sources = est_sources / est_sources.max(dim=1, keepdim=True)[0]

torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000)
torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000)

```

#### Referencing SpeechBrain

```
@misc{SB2021,
    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 },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/speechbrain/speechbrain}},
  }
```

#### Referencing SepFormer
```
@inproceedings{subakan2021attention,
      title={Attention is All You Need in Speech Separation}, 
      author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong},
      year={2021},
      booktitle={ICASSP 2021}
}
```