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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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Pronounced as "musician",

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


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: ~~~~python from musicnn.tagger import toptags toptags('./audio/joram-momentsofclarity-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

folder of this repository.

You can listen to those and evaluate


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