Package: vak
A neural network framework for animal acoustic communication and bioacousticsvakis a Python framework for neural network models,
designed for researchers studying animal acoustic communication and bioacoustics.
Many people will be familiar with work in this area on
animal vocalizations such as birdsong, bat calls, and even human speech.
Neural network models have provided a powerful new tool for researchers in this area,
as in many other fields.The library has two main goals:Make it easier for researchers studying animal vocalizations to
apply neural network algorithms to their dataProvide a common framework that will facilitate benchmarking neural
network algorithms on tasks related to animal vocalizationsCurrently, the main use is an automaticannotationof vocalizations and other animal sounds.
Byannotation, we mean something like the example of annotated birdsong shown below:You givevaktraining data in the form of audio or spectrogram files with annotations,
and thenvakhelps you train neural network models
and use the trained models to predict annotations for new files.We developedvakto benchmark a neural network model we calltweetynet.Please see the eLife article here:https://elifesciences.org/articles/63853For more background on animal acoustic communication and deep learning,
and how these intersect with related fields like
computational ethology and neuroscience,
please see the"About"section below.InstallationShort version:withpip$pipinstallvakwithconda$condainstallvak-cpytorch-cconda-forge$#                  ^ notice additional channel!Notice that forcondayou specify two channels,
and that thepytorchchannel should come first,
so it takes priority when installing the dependenciespytorchandtorchvision.For more details, please see:https://vak.readthedocs.io/en/latest/get_started/installation.htmlWe testvakon Ubuntu and MacOS. We have run on Windows and
know of other users successfully runningvakon that operating system,
but installation on Windows may require some troubleshooting.
A good place to start is by searching theissues.UsageTutorialCurrently the easiest way to work withvakis through the command line.You run it with configuration files, using one of a handful of commands.For more details, please see the "autoannotate" tutorial here:https://vak.readthedocs.io/en/latest/get_started/autoannotate.htmlHow can I use my data withvak?Please see the How-To Guides in the documentation here:https://vak.readthedocs.io/en/latest/howto/index.htmlfaq.html#faqSupport / ContributingFor help, please begin by checking out the Frequently Asked Questions:https://vak.readthedocs.io/en/latest/faq.html.To ask a question about vak, discuss its development,
or share how you are using it,
please start a new "Q&A" topic on the VocalPy forum
with the vak tag:https://forum.vocalpy.org/To report a bug, or to request a feature,
please use the issue tracker on GitHub:https://github.com/vocalpy/vak/issuesFor a guide on how you can contribute tovak, please see:https://vak.readthedocs.io/en/latest/development/index.htmlCitationIf you use vak for a publication, please cite its DOI:Licenseishere.AboutAre humans unique among animals?
We speak languages, but is speech somehow like other animal behaviors, such as birdsong?
Questions like these are answered by studying how animals communicate with sound.
This research requires cutting edge computational methods and big team science across a wide range of disciplines,
including ecology, ethology, bioacoustics, psychology, neuroscience, linguistics, and genomics^1^3.
As in many other domains, this research is being revolutionized by deep learning algorithms^1^3.
Deep neural network models enable answering questions that were previously impossible to address,
in part because these models automate analysis of very large datasets.
Within the study of animal acoustic communication, multiple models have been proposed for similar tasks,
often implemented as research code with different libraries, such as Keras and Pytorch.
This situation has created a real need for a framework that allows researchers to easily benchmark models
and apply trained models to their own data. To address this need, we developed vak.
We originally developed vak to benchmark a neural network model, TweetyNet^4,
that automates annotation of birdsong by segmenting spectrograms.
TweetyNet and vak have been used in both neuroscience^6^8and bioacoustics^9.
For additional background and papers that have usedvak,
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 onpypiyet.
Also I guess it hassomething 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📖🐛alyndanoel🤔adamfishbein📖vivinastase🐛📓kaiyaprovost💻🤔ymk12345🐛📖neuronalX🐛📖Khoa📖sthaar📖🐛🤔yangzheng-121🐛🤔lmpascual📖ItamarFruchter📖Hjalmar K. Turesson🐛🤔nhoglen🐛This project follows theall-contributorsspecification. Contributions of any kind welcome!