musiclang 0.8.12

A python package for music notation and generation

Musiclang

The Python framework to write, analyze, transform and predict music.

What is MusicLang ?

MusicLang which simply stands for "music language" is a Python framework implementing a new language for tonal music. This language allows composers to load, write, transform and predict symbolic music in a simple, condensed and high level manner. MusicLang internally uses a LLM (Large Language Model) to predict what could happen next in a musical score. This framework is well suited to :

• Generate musical ideas quickly.
• Predict what could happen next in an existing midi file
• Create an interpretable and information rich text representation of a midi file

Read our documentation.

How to install

MusicLang is available on Pypi :

pip install musiclang

Examples

1. A hello world example to create a C-major chord in musiclang and save it to midi :

from musiclang.library import *

# Write A C major chord
score = (I % I.M)(piano=[s0, s2, s4])

# Store it to midi
score.to_midi('c_major.mid')

2. Create, transform and harmonize a theme quickly :

from musiclang.library import *

# Create a cool melody (the beginning of happy birthday, independant of any harmonic context)
melody = s4.ed + s4.s + s5 + s4 + s0.o(1) + s6.h

# Create a simple accompaniment with a cello and a oboe
acc_melody = r + s0.o(-1).q * 3 + s0.o(-1).h
accomp = {'cello__0': acc_melody, 'oboe__0': acc_melody.o(1)}

# Play it in F-major
score = (I % IV.M)(violin__0=melody, **accomp)

# Repeat the score a second time in F-minor and forte
score += (score % I.m).f

# Just to create an anachrusis at the first bar
score = (I % I.M)(violin__0=r.h) + score

# Transform a bit the accompaniment by applying counterpoint rules automatically
from musiclang.transform.library import create_counterpoint_on_score
score = create_counterpoint_on_score(score, fixed_parts=['violin__0'])

# Save it to musicxml
score.to_musicxml('happy_birthday.musicxml', signature=(3, 4), title='Happy birthday !')

# Et voilà !

3. Predict a score using a deep learning model trained on musiclang language :

from musiclang.library import *
from musiclang import Score

# Some random bar of chopin op18 Waltz
score = ((V % III.b.M)(
	piano__0=s0 + s2.e.mp + s3.e.mp, 
	piano__4=s0.e.o(-2).p + r.e + s0.ed.o(-1).mp + r.s, 
	piano__5=r + s4.ed.o(-1).mp + r.s, 
	piano__6=r + s6.ed.o(-1).mp + r.s)+ 
(V['7'] % III.b.M)(
	piano__0=s2.ed.mp + r.s, 
	piano__2=s4.ed.mp + r.s, 
	piano__4=s6.ed.o(-1).mp + r.s, 
	piano__5=s0.ed.o(-1).mp + r.s, 
	piano__6=s4.ed.o(-1).mp + r.s))

# Predict the next two chords of the score using huggingface musiclang model
predicted_score = score.predict_score(n_chords=4, temperature=0.5)
# Save it to midi
predicted_score.to_midi('test.mid')

Please note that this feature is still experimental, it will only work with piano music for now and the model is not yet trained on a large corpus of music. If you want to help us train a better model, please contact us

4. Mix everything together to create a new pieces of music !