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 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 speechbrain

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

Perform source separation on your own audio file

from speechbrain.pretrained import SepformerSeparation as separator
import torchaudio

model = separator.from_hparams(source="speechbrain/sepformer-wsj02mix", savedir='pretrained_models/sepformer-wsj02mix')

# for custom file, change path
est_sources = model.separate_file(path='speechbrain/sepformer-wsj02mix/test_mixture.wav')"source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000)"source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000)

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.


The model was trained with SpeechBrain (fc2eabb7). To train it from scratch follows these steps:

  1. Clone SpeechBrain:

    git clone
  2. Install it:

    cd speechbrain
    pip install -r requirements.txt
    pip install -e .
  3. Run Training:

    cd  recipes/WSJ0Mix/separation
    python hparams/sepformer.yaml --data_folder=your_data_folder

You can find our training results (models, logs, etc) here.


The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

Referencing SpeechBrain

  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},

Referencing SepFormer

      title={Attention is All You Need in Speech Separation}, 
      author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong},
      booktitle={ICASSP 2021}

About SpeechBrain

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