Sepformer Rescuespeech

🚀 SepFormer trained on RescueSpeech for speech enhancement (16k sampling frequency)

This repository offers all the essential tools for speech enhancement (denoising) using a SepFormer model implemented with SpeechBrain. The model was initially trained on the Microsoft - DNS 4 dataset and then fine - tuned on the RescueSpeech dataset with a 16k sampling frequency. For a better experience, it's recommended to learn more about SpeechBrain.

🚀 Quick Start

This repository provides all the necessary tools to perform speech enhancement (denoising) with a SepFormer model, implemented with SpeechBrain. The model was first trained on Microsoft - DNS 4 dataset and further fine - tuned on RescueSpeech dataset 16k sampling frequency. For a better experience we encourage you to learn more about SpeechBrain. Given below is model performance on RescueSpeech test set.

where SI - SNRi and SI - SDRi indicates the improvement in SI - SNR and SI - SDR metric.

✨ Features

• Perform speech enhancement (denoising) with a SepFormer model implemented in SpeechBrain.
• The model is trained on Microsoft - DNS 4 dataset and fine - tuned on RescueSpeech dataset.

📦 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 speech enhancement on your own audio file

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

model = separator.from_hparams(source="speechbrain/rescuespeech_sepformer", savedir='pretrained_models/rescuespeech_sepformer')

# for custom file, change path
est_sources = model.separate_file(path='speechbrain/rescuespeech_sepformer/example_rescuespeech16k.wav') 

torchaudio.save("enhanced_rescuespeech16k.wav", est_sources[:, :, 0].detach().cpu(), 16000)

Advanced Usage

Inference on GPU

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

⚠️ Important Note

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

📚 Documentation

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

📄 License

This project is licensed under the "apache - 2.0" license.

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}
}

Referencing RescueSpeech

@misc{sagar2023rescuespeech,
    title={RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain},
    author={Sangeet Sagar and Mirco Ravanelli and Bernd Kiefer and Ivana Kruijff Korbayova and Josef van Genabith},
    year={2023},
    eprint={2306.04054},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}

🔗 About SpeechBrain

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