Metricgan Plus Voicebank

🚀 MetricGAN-trained model for Enhancement

This repository offers all the essential tools for speech enhancement using SpeechBrain. For an optimal experience, we recommend learning more about SpeechBrain. The model's performance metrics are as follows:

🚀 Quick Start

This project provides tools for speech enhancement with SpeechBrain. For a better understanding, explore SpeechBrain.

✨ Features

• Tags: audio-to-audio, speech-enhancement, PyTorch, speechbrain
• License: apache-2.0
• Datasets: Voicebank, DEMAND
• Metrics: PESQ, STOI

📦 Installation

First, install SpeechBrain using the following command:

pip install speechbrain

We encourage you to read our tutorials and learn more about SpeechBrain.

💻 Usage Examples

Basic Usage

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

import torch
import torchaudio
from speechbrain.inference.enhancement import SpectralMaskEnhancement

enhance_model = SpectralMaskEnhancement.from_hparams(
    source="speechbrain/metricgan-plus-voicebank",
    savedir="pretrained_models/metricgan-plus-voicebank",
)

# Load and add fake batch dimension
noisy = enhance_model.load_audio(
    "speechbrain/metricgan-plus-voicebank/example.wav"
).unsqueeze(0)

# Add relative length tensor
enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))

# Saving enhanced signal on disk
torchaudio.save('enhanced.wav', enhanced.cpu(), 16000)

Note: 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. Ensure your input tensor complies with the expected sampling rate when using enhance_batch as in the example.

Advanced Usage

Inference on GPU

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

🔧 Technical Details

Training

The model was trained with SpeechBrain (d0accc8). To train it from scratch, follow 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/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.

📚 Documentation

Referencing MetricGAN+

If you find MetricGAN+ useful, please cite:

@article{fu2021metricgan+,
  title={MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement},
  author={Fu, Szu-Wei and Yu, Cheng and Hsieh, Tsun-An and Plantinga, Peter and Ravanelli, Mirco and Lu, Xugang and Tsao, Yu},
  journal={arXiv preprint arXiv:2104.03538},
  year={2021}
}

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}
}

📄 License

This project is licensed under the apache-2.0 license.