metadata

language: en
tags:
  - Speech Enhancement
  - PyTorch
license: apache-2.0
datasets:
  - Voicebank
  - DEMAND
metrics:
  - PESQ
  - STOI

MetricGAN-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:

Release	Test PESQ	Test STOI
21-04-27	3.15	93.0

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:

import torch
import torchaudio
from speechbrain.pretrained 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)

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:

Clone SpeechBrain:

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

Install it:

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

Run Training:

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

https://drive.google.com/drive/folders/1fcVP52gHgoMX9diNN1JxX_My5KaRNZWs?usp=sharing

You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1fcVP52gHgoMX9diNN1JxX_My5KaRNZWs?usp=sharing)

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

## 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} }


## Referencing SpeechBrain

If you find SpeechBrain useful, please cite:

@misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/speechbrain/speechbrain}}, }


#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain