Pronounced as "musician", musicnn is a set of pre-trained deep convolutional neural networks for music audio tagging.
Pronounced as "musician",
musicnnis a set of pre-trained musically motivated convolutional neural networks for music audio tagging. This repository also includes some pre-trained vgg-like baselines.
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 https://github.com/jordipons/musicnn.git
python setup.py install
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 file_name.au --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
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