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.


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!