vak 0.4.0b6

a neural network toolbox for animal vocalizations and bioacoustics

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. While there are many important reasons to study bioacoustics, the scope of vak is limited to questions related to vocal learning, "the ability to modify acoustic and syntactic sounds, acquire new sounds via imitation, and produce vocalizations" (Wikipedia). Research questions related to vocal learning cut across a wide range of fields including neuroscience, phsyiology, molecular biology, genomics, ecology, and evolution (Wirthlin et al. 2019).

vak 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, using artificial neural networks. 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:

$ pip install vak

For the long version detail, 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 will probably require some troubleshooting. A good place to start is by searching the issues.

Usage

Training models to segment and label vocalizations

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

Data and folder structures

To train models, you provide training data in the form of audio or spectrograms files, and annotations for those files.

Spectrograms and labels

The package can generate spectrograms from .wav files or .cbin audio files. It can also accept spectrograms in the form of Matlab .mat or Numpy .npz files. The locations of these files are specified in the config.toml file.

The annotations are parsed by a separate library, crowsetta, that aims to handle common formats like Praat textgrid files, and enable researchers to easily work with formats they may have developed in their own labs. For more information please see:
https://crowsetta.readthedocs.io/en/latest/
https://github.com/NickleDave/crowsetta

Preparing training files

It is possible to train on any manually annotated data but there are some useful guidelines:

• Use as many examples as possible - The results will just be better. Specifically, this code will not label correctly syllables it did not encounter while training and will most probably generalize to the nearest sample or ignore the syllable.
• Use noise examples - This will make the code very good in ignoring noise.
• Examples of syllables on noise are important - It is a good practice to start with clean recordings. The code will not perform miracles and is most likely to fail if the audio is too corrupt or masked by noise. Still, training with examples of syllables on the background of cage noises will be beneficial.

Predicting annotations for audio

You can predict annotations for audio files by creating a config.toml file with a [PREDICT] section.
For more details, please see the "autoannotate" tutorial here: https://vak.readthedocs.io/en/latest/tutorial/autoannotate.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

"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.