speechbrain / sgmse-voicebank

SGMSE-trained model for Enhancement

This repository provides all the necessary tools to perform enhancement with SpeechBrain. For a better experience we encourage you to learn more about SpeechBrain . The model performance is:

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 .

Pretrained Usage

To use the mimic-loss-trained model for enhancement, use the following simple code:

from speechbrain.inference.enhancement import SGMSEEnhancement

enhance_model = SGMSEEnhancement.from_hparams(
    source="speechbrain/sgmse-voicebank",
    savedir="pretrained_models/sgmse-voicebank",
)

enhanced = enhance_model.enhance_file("speechbrain/sgmse-voicebank/example.wav")

The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling enhance_file if needed. Make sure your input tensor is compliant with the expected sampling rate if you use enhance_batch as in the example.

Inference on GPU

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

Training

The model was trained with SpeechBrain (d0accc8). To train it from scratch follows these steps:

1. Clone SpeechBrain:

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

1. Install it:

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

1. Run Training:

cd  recipes/Voicebank/enhance/MetricGAN
python train.py hparams/train.yaml --data_folder=your_data_folder

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 SGMSE

If you find SGMSE useful, please cite:

@article{richter2023speech,
  title={Speech enhancement and dereverberation with diffusion-based generative models},
  author={Richter, Julius and Welker, Simon and Lemercier, Jean-Marie and Lay, Bunlong and Gerkmann, Timo},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  volume={31},
  pages={2351--2364},
  year={2023},
  publisher={IEEE}
}

About SpeechBrain

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

Citing SpeechBrain

Please, cite SpeechBrain if you use it for your research or business.

@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}
}