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vak

A neural network framework for animal acoustic communication and bioacoustics.
https://github.com/vocalpy/vak

Category: Biosphere
Sub Category: Bioacoustics and Acoustic Data Analysis

Keywords

animal-communication animal-vocalizations bioacoustic-analysis bioacoustics birdsong python python3 pytorch spectrograms speech-processing torch torchvision vocalizations

Keywords from Contributors

vectors profiles csv simulator charts changed-file orchestration transforms alert tracing

Last synced: about 1 hour ago
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A neural network framework for researchers studying acoustic communication

README.md

A neural network framework for researchers studying acoustic communication

DOI

All Contributors

PyPI version
License
Build Status
codecov

vak is a Python framework for neural network models,
designed for researchers studying acoustic communication:
how and why animals communicate with sound.
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:

  1. Make it easier for researchers studying acoustic communication to
    apply neural network algorithms to their data
  2. Provide a common framework that will facilitate benchmarking neural
    network algorithms on tasks related to acoustic communication

Currently, the main use is an automatic 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.
Please see the eLife article here: https://elifesciences.org/articles/63853

To learn more about the goals and design of vak,
please see this talk from the SciPy 2023 conference,
and the associated Proceedings paper
here.

For 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.

Installation

Short version:

with pip

$ pip install vak

with conda

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

Notice that for conda you specify two channels,
and that the pytorch channel should come first,
so it takes priority when installing the dependencies pytorch and torchvision.

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

We 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.
terminal showing vak help command output

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.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/index.html

Support / Contributing

For 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/issues

For a guide on how you can contribute to vak, please see:
https://vak.readthedocs.io/en/latest/development/index.html

Citation

If you use vak for a publication, please cite both the Proceedings paper and the software.

Proceedings paper (BiBTex)

@inproceedings{nicholson2023vak,
  title={vak: a neural network framework for researchers studying animal acoustic communication},
  author={Nicholson, David and Cohen, Yarden},
  booktitle={Python in Science Conference},
  pages={59--67},
  year={2023}
}

Software

DOI

License

License
is here.

About

Are 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 ^6^8 and bioacoustics ^9.
For additional background and papers that have used 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):

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

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: vak
message: >-
  a neural network toolbox for animal vocalizations
  and bioacoustics 
type: software
authors:
  - given-names: David
    family-names: Nicholson
    email: [email protected]
    affiliation: Emory University
    orcid: 'https://orcid.org/0000-0002-4261-4719'
  - given-names: Yarden
    family-names: Cohen
    orcid: 'https://orcid.org/0000-0002-8149-6954'
    affiliation: Weizmann Institute
    email: [email protected]
identifiers:
  - type: doi
    value: 10.5281/zenodo.5828090
repository-code: 'https://github.com/NickleDave/vak'
url: 'https://vak.readthedocs.io'
repository-artifact: 'https://pypi.org/project/vak/'
keywords:
  - python
  - animal vocalizations
  - neural networks
  - bioacoustics
license: BSD-3-Clause
commit: ad802dcad34b524533b765e5dfb3709b308a3152
version: 0.4.2
date-released: '2022-03-29'

Owner metadata

GitHub Events

Total
Last Year

Committers metadata

Last synced: 5 days ago

Total Commits: 2,576
Total Committers: 10
Avg Commits per committer: 257.6
Development Distribution Score (DDS): 0.212

Commits in past year: 32
Committers in past year: 4
Avg Commits per committer in past year: 8.0
Development Distribution Score (DDS) in past year: 0.5

Name Email Commits
David Nicholson n****e 2031
NickleDave n****v@g****m 431
allcontributors[bot] 4****] 57
yardencsGitHub y****c@b****u 51
milaXT 1****T 1
kaiyaprovost 1****t 1
Luke Poeppel l****l@g****m 1
Khoa 5****7 1
Ja-sonYun k****7@g****m 1
Ikko Ashimine e****r@g****m 1

Committer domains:

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 160
Total pull requests: 89
Average time to close issues: 7 months
Average time to close pull requests: 4 days
Total issue authors: 16
Total pull request authors: 8
Average comments per issue: 1.56
Average comments per pull request: 0.79
Merged pull request: 81
Bot issues: 0
Bot pull requests: 17

Past year issues: 26
Past year pull requests: 12
Past year average time to close issues: 20 days
Past year average time to close pull requests: about 1 hour
Past year issue authors: 5
Past year pull request authors: 3
Past year average comments per issue: 1.62
Past year average comments per pull request: 0.33
Past year merged pull request: 12
Past year bot issues: 0
Past year bot pull requests: 5

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/vocalpy/vak

Top Issue Authors

  • NickleDave (137)
  • athenasyarifa (3)
  • harshidapancholi (3)
  • yardencsGitHub (3)
  • henricombrink (2)
  • milaXT (2)
  • meriablue (1)
  • nhoglen (1)
  • cantonsir (1)
  • wendtalexander (1)
  • avanikop (1)
  • vivinastase (1)
  • danielmk (1)
  • kalleknast (1)
  • ItamarFruchter (1)

Top Pull Request Authors

  • NickleDave (65)
  • allcontributors[bot] (17)
  • marisbasha (2)
  • nosrednab (1)
  • zhileiz1992 (1)
  • milaXT (1)
  • TrellixVulnTeam (1)
  • Ja-sonYun (1)

Top Issue Labels

  • ENH: enhancement (47)
  • BUG (23)
  • DOC: documentation (17)
  • Models (11)
  • TST: testing (8)
  • CLN: clean / refactor (7)
  • DEV: development (5)
  • CI: continuous integration (3)
  • api (1)
  • dependencies (1)
  • Metrics (1)
  • Datasets (1)

Top Pull Request Labels

  • DOC: documentation (1)

Package metadata

pypi.org: vak

A neural network framework for researchers studying acoustic communication

  • Homepage:
  • Documentation: https://vak.readthedocs.io
  • Licenses: BSD License
  • Latest release: 1.0.3 (published 7 months ago)
  • Last Synced: 2025-05-13T23:05:42.660Z (1 day ago)
  • Versions: 45
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 930 Last month
  • Rankings:
    • Dependent packages count: 3.271%
    • Stargazers count: 8.458%
    • Forks count: 9.146%
    • Average: 11.971%
    • Downloads: 16.746%
    • Dependent repos count: 22.233%
  • Maintainers (1)
conda-forge.org: vak

  • Homepage: https://pypi.org/project/vak/
  • Licenses: BSD-3-Clause
  • Latest release: 0.6.0 (published almost 3 years ago)
  • Last Synced: 2025-05-01T02:53:53.006Z (14 days ago)
  • Versions: 4
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Rankings:
    • Dependent packages count: 28.82%
    • Dependent repos count: 34.025%
    • Average: 36.017%
    • Stargazers count: 39.052%
    • Forks count: 42.171%

Dependencies

pyproject.toml pypi
  • SoundFile >=0.10.3
  • attrs >=19.3.0
  • crowsetta >=5.0.1
  • dask >=2.10.1
  • evfuncs >=0.3.4
  • joblib >=0.14.1
  • matplotlib >=3.3.3
  • numpy >=1.18.1
  • pandas >=1.0.1
  • pynndescent >=0.5.10
  • pytorch-lightning >=2.0.7
  • scipy >=1.4.1
  • tensorboard >=2.8.0
  • toml >=0.10.2
  • torch >= 2.0.1
  • torchvision >=0.15.2
  • tqdm >=4.42.1
  • umap-learn >=0.5.3
.github/workflows/ci-linux.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v3 composite
  • excitedleigh/setup-nox v2.1.0 composite

Score: 14.503353696932315