vak

a neural network toolbox for animal vocalizations and bioacoustics

vak is a library for researchers studying animal vocalizations--such as birdsong, bat calls, and even human speech--although it may be useful to anyone working with bioacoustics data.

The library has two main goals:

1. make it easier for researchers studying animal vocalizations to apply neural network algorithms to their data
2. provide a common framework that will facilitate benchmarking neural network algorithms on tasks related to animal vocalizations

Currently the main use is automated annotation of vocalizations and other animal sounds. By annotation, we mean something like the example of annotated birdsong shown below:

You give vak training data in the form of audio or spectrogram files with annotations, and then vak helps you train neural network models and use the trained models to predict annotations for new files.

We developed vak to benchmark a neural network model we call tweetynet. See pre-print here: https://www.biorxiv.org/content/10.1101/2020.08.28.272088v2.full.pdf
We would love to help you use vak to benchmark your own model. If you have questions, please feel free to raise an issue.

Installation

Short version:

with pip

$ pip install vak

with conda

on Mac and Linux

$ conda install vak -c conda-forge

on Windows

On Windows, you need to add an additional channel, pytorch.
You can do this by repeating the -c option more than once.

$ conda install vak -c conda-forge -c pytorch
$ #                                 ^ notice additional channel!

For more details, please see: https://vak.readthedocs.io/en/latest/get_started/installation.html

We currently test vak on Ubuntu and MacOS. We have run on Windows and know of other users successfully running vak on that operating system, but installation on Windows may require some troubleshooting. A good place to start is by searching the issues.

Usage

Tutorial

Currently the easiest way to work with vak is through the command line.

You run it with config.toml files, using one of a handful of commands.

For more details, please see the "autoannotate" tutorial here:
https://vak.readthedocs.io/en/latest/tutorial/autoannotate.html

How can I use my data with vak?

Please see the How-To Guides in the documentation here: https://vak.readthedocs.io/en/latest/howto/howto.html

Support / Contributing

Currently we are handling support through the issue tracker on GitHub:
https://github.com/NickleDave/vak/issues
Please raise an issue there if you run into trouble.
That would be a great place to start if you are interested in contributing, as well.

Citation

If you use vak for a publication, please cite its DOI:

License

is here.

Misc

For more on the history of vak please see: https://vak.readthedocs.io/en/latest/reference/about.html

"Why this name, vak?"

It has only three letters, so it is quick to type, and it wasn't taken on pypi yet. Also I guess it has something to do with speech. "vak" rhymes with "squawk" and "talk".

Does your library have any poems?

Yes.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

avanikop 🐛

Luke Poeppel 📖

yardencsGitHub 💻 🤔 📢 📓 💬

David Nicholson 🐛 💻 🔣 📖 💡 🤔 🚇 🚧 🧑‍🏫 📆 👀 💬 📢 ⚠️ ✅

marichard123 📖

Therese Koch 📖 🐛

This project follows the all-contributors specification. Contributions of any kind welcome!