neural network that segments and labels birdsong and other animal vocalizations
A neural network architecture (shown below) that automates annotation of birdsong and other vocalizations by segmenting spectrograms, and then labeling those segments.
This is an example of the kind of annotations that
tweetynet learns to predict:
How is it used?
Short version (for details see below):
$ pip install tweetynet
on Mac and Linux
$ conda install tweetynet -c conda-forge
On Windows, you need to add an additional channel,
You can do this by repeating the
-c option more than once.
$ conda install tweetynet -c conda-forge -c pytorch $ # ^ notice additional channel!
To facilitate training
tweetynet models and using trained models
to predict annotation on new datasets,
we developed the
that is installed automatically with
If you need more information about installation, please see the
To train models and use them to predict annotation
For a tutorial on using
vak, please see the
To reproduce results from article
In the directory
we provide code to reproduce the results in the article
"TweetyNet: A neural network that enables high-throughput, automated annotation of birdsong"
Please see the README in that directory for instructions on how to install and work with that code.
To train models, you must supply training data in the form of audio files or
spectrogram files, and annotations.
The package can generate spectrograms from
.cbin audio files.
It can also accept spectrograms in the form of Matlab
.mat files or
.npz files created by
vak uses a separate library to parse annotations,
which handles some common formats and can also be used to write custom parsers for other formats.
Please see the
crowsetta documentation for more detail:
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.
For more details, please see the vak documentation.
If you run into problems, please use the issue tracker or contact the authors via email in the paper above.
Released under BSD license.
Thanks goes to these wonderful people (emoji key):
💻 🐛 🔣 📖 🤔 💬 🔧 ⚠️ ✅ 📢
💻 🐛 🔣 📖 🤔 💬 🔧 ⚠️ ✅ 📢
This project follows the all-contributors specification. Contributions of any kind welcome!
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