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a library for benchmarking neural networks that segment and annotate

Build Status


To install, run the following command at the command line:
pip install vak

Before you install, you'll want to set up a virtual environment (for an explanation of why, see Creating a virtual environment is not as hard as it might sound; here's a primer on Python tools:
For many scientific packages that depend on libraries written in
languages besides Python, you may find it easier to use a platform dedicated to managing those dependencies, such as Anaconda (which is free). You can use the conda command-line tool that they develop
to create environments and install the scientific libraries that this package depends on. In addition, using conda to install the dependencies may give you some performance gains (see
Here's how you'd set up a conda environment:
/home/you/code/ $ conda create -n vak-env python=3.5 numpy scipy joblib tensorflow-gpu ipython jupyter
/home/you/code/ $ source activate vak-env
(You don't have to source on Windows: > activate vak-env)

You can then use pip inside a conda environment:
(vak-env)/home/you/code/ $ pip install vak

You can also work with a local copy of the code. It's possible to install the local copy with pip so that you can still edit the code, and then have its behavior as an installed library reflect those edits.

  • Clone the repo from Github using the version control tool git:
    (vak-env)/home/you/code/ $ git clone
    (you can install git from Github or using conda.)
  • Install the package with pip using the -e flag (for editable).
    $ (vak-env)/home/you/code/ $ cd tf_syllable_segmentation_annotation
    $ (vak-env) pip install -e .


Training models to segment and label birdsong

To train models, use the command line interface, vak-cli. You run it with config.ini files, using one of a handful of command-line flags. Here's the help text that prints when you run $ vak-cli --help:

main script

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG, --config CONFIG
                        run learning curve experiment with a single config.ini file, by passing the name of that file.
                        $ vak-cli --config ./config_bird1.ini
  -g GLOB, --glob GLOB  string to use with glob function to search for config files fitting some pattern.
                        $ vak-cli --glob ./config_finches*.ini
  -p PREDICT, --predict PREDICT
                        predict segments and labels for song, using a trained model specified in a single config.ini file
                        $ vak-cli --predict ./predict_bird1.ini
  -s SUMMARY, --summary SUMMARY
                        runs function that summarizes results from generatinga learning curve, using a single config.ini file
                        $ vak-cli --summary ./config_bird1.ini
  -t TXT, --txt TXT     name of .txt file containing list of config files to run
                        $ vak-cli --text ./list_of_config_filenames.txt

As an example, you can run vak-cli with a single config.ini file by using the --config flag and passing the name of the config.ini file as an argument:
(vak-env)$ vak-cli --config ./configs/config_bird0.ini

For more details on how training works, see, and for more details on the config.ini files, see

Data and folder structures

To train models, you must supply training data in the form of audio files or spectrograms, and annotations for each spectrogram.

Spectrograms and labels

The package can generate spectrograms from .wav files or .cbin files. It can also accept spectrograms in the form of Matlab .mat files. The locations of these files are specified in the config.ini file as explained in and

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.

Results of running the code

It is recommended to apply post processing when extracting the actual syllable tag and onset and offset timesfrom the estimates.

Predicting new labels

You can predict new labels by adding a [PREDICT] section to the config.ini file, and then running the command-line interface with the --predict flag, like so:
(vak-env)$ vak-cli --predict ./configs/config_bird0.ini An example of what a config.ini file with a [PREDICT] section is in the doc folder here.

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