language: en
thumbnail: null
tags:
- Source Separation
- Speech Separation
- Audio Source Separation
- WSJ02Mix
- SepFormer
- Transformer
license: apache-2.0
datasets:
- WSJ0-2Mix
metrics:
- SI-SNRi
- SDRi
SepFormer trained on WSJ0-2Mix
This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about SpeechBrain. The given model performance is 22.4 dB on the test set of WSJ0-2Mix dataset.
| Release | Test-Set SI-SNRi | Test-Set SDRi |
|---|---|---|
| 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.
Perform source separation on your own audio file```python
from speechbrain.pretrained import separator import torchaudio
model = separator.from_hparams(source="speechbrain/sepformer-wsj02mix")
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} } ```