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Asteroid is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers.
Please, if you have found a bug, open an issue, if you solved it, open a pull request! Same goes for new features, tell us what you want or help us building it! Don't hesitate to join the slack and ask questions / suggest new features there as well! Asteroid is intended to be a community-based project so hop on and help us!
(↑up to contents) To install Asteroid, clone the repo and install it using conda, pip or python :
# First clone and enter the repo
git clone https://github.com/asteroid-team/asteroid
cd asteroid
pip
# Install with pip in editable mode
pip install -e .
# Or, install with python in dev mode
# python setup.py develop
conda env create -f environment.yml
conda activate asteroid
pip install asteroid
(↑up to contents) Here is a list of notebooks showing example usage of Asteroid's features.
PITLossWrapper
(↑up to contents) Running the recipes requires additional packages in most cases, we recommend running :
# from asteroid/
pip install -r requirements.txt
Then choose the recipe you want to run and run it!
cd egs/wham/ConvTasNet
. ./run.sh
More information in egs/README.md.
(↑up to contents) See here
(↑up to contents) We are always looking to expand our coverage of the source separation and speech enhancement research, the following is a list of things we're missing. You want to contribute? This is a great place to start!
Don't forget to read our contributing guidelines.
You can also open an issue or make a PR to add something we missed in this list.
The default logger is TensorBoard in all the recipes. From the recipe folder, you can run the following to visualize the logs of all your runs. You can also compare different systems on the same dataset by running a similar command from the dataset directiories.
# Launch tensorboard (default port is 6006)
tensorboard --logdir exp/ --port tf_port
If your launching tensorboard remotely, you should open an ssh tunnel
# Open port-forwarding connection. Add -Nf option not to open remote.
ssh -L local_port:localhost:tf_port user@ip
Then open http://localhost:local_port/
. If both ports are the same, you can
click on the tensorboard URL given on the remote, it's just more practical.
(↑up to contents) If you loved using Asteroid and you want to cite us, use this :
@inproceedings{Pariente2020Asteroid,
title={Asteroid: the {PyTorch}-based audio source separation toolkit for researchers},
author={Manuel Pariente and Samuele Cornell and Joris Cosentino and Sunit Sivasankaran and
Efthymios Tzinis and Jens Heitkaemper and Michel Olvera and Fabian-Robert Stöter and
Mathieu Hu and Juan M. Martín-Doñas and David Ditter and Ariel Frank and Antoine Deleforge
and Emmanuel Vincent},
year={2020},
booktitle={Proc. Interspeech},
}
FAQs
PyTorch-based audio source separation toolkit
We found that asteroid demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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