Sepformer Wsj03mix

🚀 SepFormer trained on WSJ0-3Mix

This repository offers all the essential tools for audio source separation using a SepFormer model implemented with SpeechBrain and pre - trained on the WSJ0 - 3Mix dataset. For a better experience, it's recommended to learn more about SpeechBrain. The model achieves 19.8 dB SI - SNRi on the test set of the WSJ0 - 3Mix dataset.

🚀 Quick Start

This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0 - 3Mix dataset. For a better experience we encourage you to learn more about SpeechBrain. The model performance is 19.8 dB SI - SNRi on the test set of WSJ0 - 3Mix dataset.

✨ Features

• Tags: Source Separation, Speech Separation, Audio Source Separation, WSJ0 - 3Mix, SepFormer, Transformer, audio - to - audio, audio - source - separation, speechbrain
• License: apache - 2.0
• Datasets: WSJ0 - 3Mix
• Metrics: SI - SNRi, SDRi

📦 Installation

Install SpeechBrain

First of all, please install SpeechBrain with the following command:

pip install speechbrain

💡 Usage Tip

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

💻 Usage Examples

Basic Usage

Perform source separation on your own audio file:

from speechbrain.inference.separation import SepformerSeparation as separator
import torchaudio

model = separator.from_hparams(source="speechbrain/sepformer-wsj03mix", savedir='pretrained_models/sepformer-wsj03mix')

est_sources = model.separate_file(path='speechbrain/sepformer-wsj03mix/test_mixture_3spks.wav') 

torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000)
torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000)
torchaudio.save("source3hat.wav", est_sources[:, :, 2].detach().cpu(), 8000)

⚠️ Important Note

The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.

Advanced Usage

Inference on GPU

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

📚 Documentation

Training

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

1. Clone SpeechBrain:

git clone https://github.com/speechbrain/speechbrain/

2. Install it:

cd speechbrain
pip install -r requirements.txt
pip install -e .

3. Run Training:

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

⚠️ Important Note

Note: change num_spks to 3 in the yaml file.

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

Limitations

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

Referencing

Referencing SpeechBrain

@misc{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},
  year={2021},
  eprint={2106.04624},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  note={arXiv:2106.04624}
}

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}
}

📄 License

This project is licensed under the apache - 2.0 license.

🔗 About SpeechBrain

• Website: https://speechbrain.github.io/
• Code: https://github.com/speechbrain/speechbrain/
• HuggingFace: https://huggingface.co/speechbrain/