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---
language: "en"
thumbnail:
tags:
- Source Separation
- Speech Separation
- WSJ02Mix
- SepFormer
- Transformer
license: "apache-2.0"
datasets:
- WSJ0-2Mix
metrics:
- SI-SNR
- SDR
---
# SepFormer trained on WSJ0-2Mix
This repository provides all the necessary tools to perform source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2)
model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The given model performance is 22.5 dB on the test set of WSJ0-2Mix dataset.
## 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).
### Transcribing your own audio files
```python
from speechbrain.pretrained import separator
import torchaudio
model = separator.from_hparams(source="speechbrain/sepformer-wsj02mix")
mix, fs = torchaudio.load("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}},
}
``` |