Pytorch based speech enhancement toolkit.
Go to file
shahules786 ceb69a09c3 gitattr 2022-11-23 17:32:13 +05:30
.github/workflows ci-cd 2022-11-14 16:31:51 +05:30
mayavoz rename to train 2022-11-15 21:51:45 +05:30
notebooks notebooks 2022-11-21 21:43:28 +05:30
recipes rename dataset 2022-11-15 14:33:27 +05:30
tests tests 2022-11-15 21:39:47 +05:30
.flake8 rename package 2022-11-14 11:37:26 +05:30
.gitattributes gitattr 2022-11-23 17:32:13 +05:30
.gitignore gitignore 2022-11-15 21:39:35 +05:30
.pre-commit-config.yaml rename package 2022-11-14 11:37:26 +05:30
LICENSE add license 2022-11-10 16:27:55 +05:30
MANIFEST.in include files 2022-11-23 17:09:49 +05:30
README.md update readme 2022-11-23 17:27:43 +05:30
environment.yml rename package 2022-11-14 11:37:26 +05:30
pyproject.toml toml 2022-10-05 20:42:51 +05:30
requirements.txt revert to torchmetric pesq 2022-10-26 21:46:19 +05:30
setup.cfg setup 2022-11-23 17:10:20 +05:30
setup.py rename package 2022-11-14 11:37:26 +05:30
version.txt version 2022-10-06 17:24:55 +05:30

README.md

GitHub Workflow Status GitHub GitHub issues GitHub Repo stars GitHub all releases

mayavoz is a Pytorch-based opensource toolkit for speech enhancement. It is designed to save time for audio researchers. Is provides easy to use pretrained audio enhancement models and facilitates highly customisable model training.

| Quick Start | Installation | Tutorials | Available Recipes | Demo

Key features 🔑

  • Various pretrained models nicely integrated with huggingface 🤗 that users can select and use without any hastle.
  • 📦 Ability to train and validation your own custom speech enhancement models with just under 10 lines of code!
  • 🪄 A command line tool that facilitates training of highly customisable speech enhacement models from the terminal itself!
  • Supports multi-gpu training integrated with Pytorch Lightning.

Quick Start 🔥

from mayavoz.models import Mayamodel

model = Mayamodel.from_pretrained("shahules786/mayavoz-waveunet-valentini-28spk")
model("noisy_audio.wav")

Recipes

Model Dataset STOI PESQ URL
WaveUnet Vctk-28spk 0.836 2.78 shahules786/mayavoz-waveunet-valentini-28spk
Demucs Vctk-28spk 0.961 2.56 shahules786/mayavoz-demucs-valentini-28spk
DCCRN Vctk-28spk 0.724 2.55 shahules786/mayavoz-dccrn-valentini-28spk
Demucs DNS2020 (20hrs) 0.56 1.26 shahules786/mayavoz-demucs-dns2020-20hr

Test scores are based on respective test set associated with train dataset.

Installation

Only Python 3.8+ is officially supported (though it might work with Python 3.7)

  • With Pypi
pip install mayavoz
  • With conda
conda env create -f environment.yml
conda activate mayavoz
  • From source code
git clone url
cd mayavoz
pip install -e .

Support

For commercial enquiries and scientific consulting, please contact me.

Acknowledgements

Sincere gratitude to AMPLYFI for supporting this project.