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Pronounced as "musician", musicnn is a set of pre-trained deep convolutional neural networks for music audio tagging.
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audio Notebook functioning with basic demo Jul 8, 2019
images Debugging functions, and renaming tagging_example.ipynb Jul 31, 2019
musicnn Update Oct 15, 2019
.gitignore Adding new MSD model and VGGs Jul 26, 2019 Update Aug 1, 2019 Update Sep 17, 2019 Minor fixes for pushing to PyPI Jul 18, 2019 Update Aug 18, 2019 Update Aug 1, 2019
musicnn_example.ipynb Updating notebooks Jul 30, 2019
vgg_example.ipynb Updating notebooks Jul 30, 2019


Pronounced as "musician", musicnn is a set of pre-trained musically motivated convolutional neural networks for music audio tagging. This repository also includes some pre-trained vgg-like baselines.

Check the documentation and our basic / advanced examples to understand how to use musicnn.

Do you have questions? Check the FAQs.


pip install musicnn

or, to get bigger models and all the documentation (including jupyter notebooks), install from source:

git clone

python install

Predict tags

From within python, you can estimate the topN tags:

from musicnn.tagger import top_tags
top_tags('./audio/joram-moments_of_clarity-08-solipsism-59-88.mp3', model='MTT_musicnn', topN=10)

['techno', 'electronic', 'synth', 'fast', 'beat', 'drums', 'no vocals', 'no vocal', 'dance', 'ambient']

Let's try another song!


['guitar', 'piano', 'fast']

From the command-line, you can also print the topN tags on the screen:

python -m musicnn.tagger file_name.ogg --print
python -m musicnn.tagger --model 'MSD_musicnn' --topN 3 --length 3 --overlap 1.5 --print

or save to a file:

python -m musicnn.tagger file_name.wav --save out.tags
python -m musicnn.tagger file_name.mp3 --model 'MTT_musicnn' --topN 10 --length 3 --overlap 1 --print --save out.tags

Extract the Taggram

You can also compute the taggram using python (see our basic example for more details on how to depict it):

from musicnn.extractor import extractor
taggram, tags = extractor('./audio/joram-moments_of_clarity-08-solipsism-59-88.mp3', model='MTT_musicnn')


The above analyzed music clips are included in the ./audio/ folder of this repository.

You can listen to those and evaluate musicnn yourself!

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