musicnn
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.
Installation
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
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!
top_tags('./audio/TRWJAZW128F42760DD_test.mp3')
['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
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!
