|
--- |
|
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} |
|
} |
|
``` |
