/Intelligibility-MetricGAN

Implementation for paper "iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric Learning"

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

Speech Intelligibility Enhancement using GAN

Implementation of the paper: iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric Learning

Audio samples

Usage steps

1. Install dependencies

pip install -r requirements.txt

2. Prepare training data

Prepare your training data and change data path in MultiGAN.py

A toy dataset format example is given in ./database

3. Training

run: python MultiGAN.py

Training configurations can be modified according to your need, e.g. GAN_epoch, num_of_sampling

models will be saved in ./chkpt

4. Inference

Prepare the test data, then change paths in inference.py

run: python inference.py

A pre-trained model is provided in ./trained_model
It was trained using 44.1 kHz speech materials at RMS=0.02. So please normalize your 44.1kHz raw speech input to RMS=0.02 if you would like to use this pre-trained model.

Authors

Acknowlegment

This work was partially supported by a JST CREST Grant (JPMJCR18A6, VoicePersonae project), Japan, and by MEXT KAKENHI Grants (16H06302, 17H04687, 18H04120, 18H04112, 18KT0051, 19K24372), Japan. The numerical calculations were carried out on the TSUBAME 3.0 supercomputer at the Tokyo Institute of Technology.

This project was partially based on MetricGAN codes.

License

BSD 3-Clause License

Copyright (c) 2020, Yamagishi Laboratory, National Institute of Informatics All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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