File size: 2,468 Bytes
9bc5f35 19e48e6 9bc5f35 6ac2d26 19e48e6 9bc5f35 19e48e6 9bc5f35 6ac2d26 9bc5f35 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
---
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 22.4 dB on the test set of WSJ0-2Mix dataset.
| Release | Test-Set SI-SNR | Test-Set SDR |
|:-------------:|:--------------:|:--------------:|
| 09-03-21 | 22.4dB | 22.6dB |
## 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("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}
}
``` |