A Python library for symbolic music generation, analysis and visualization.
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The modules contained in this library are:
- Loaders
contains the basic initialization to import files.
from musicaiz.loaders import Musa
midi = Musa(
file="my_midifile.mid",
structure="bars"
)- Structure
contains the structure elements in music (instruments, bars and notes).
# Define a Note object
from musicaiz.structure import Note
note = Note(
pitch=12,
start=0.0,
end=1.0,
velocity=75,
bpm=120,
resolution=96
)- Harmony
contains the harmonic elements in music (intervals, chords and keys).
from musicaiz.structure import Chords, Tonality
# Initialize a chord by its notation
chord_name = "Cm7b5"
chord = Chord(chord_name)
# get the notes in the chord
chord.get_notes(inversion=0)
# Initialize Tonality
tonality = Tonality.E_MINOR
# get different scales
tonality.natural
tonality.harmonic
tonality.melodic
# get the notes in a scale
tonality.scale_notes("NATURAL")
# get a chord from a scale degree
Tonality.get_chord_from_degree(
tonality="E_MINOR",
degree="V",
scale="NATURAL",
chord_type="triad",
)- Rhythm
contains rhythmic or timing elements in music (quantization). - Features
contains classic features to analyze symbolic music data (pitch class histograms...). - Algorithms
contains algorithms for chord prediction, key prediction, harmonic transposition... - Plotters
contains different ways of plotting music (pinorolls or scores).
from musicaiz.plotters import Pianoroll, PianorollHTML
# Matplotlib
musa_obj = Musa(midi_sample)
plot = Pianoroll(musa_obj)
plot.plot_instruments(
program=[48, 45],
bar_start=0,
bar_end=4,
print_measure_data=True,
show_bar_labels=False,
show_grid=False,
show=True,
)
# Pyplot HTML
musa_obj = Musa(midi_sample)
plot = PianorollHTML(musa_obj)
plot.plot_instruments(
program=[48, 45],
bar_start=0,
bar_end=4,
show_grid=False,
show=False
)- Tokenizers
contains different encodings to prepare symbolic music data to train a sequence model.
from musicaiz.tokenizers import MMMTokenizer, MMMTokenizerArguments
# Tokenize file
midi = "my_midifile.mid"
args = MMMTokenizerArguments(
windowing=True,
time_unit="SIXTEENTH",
num_programs=None,
shuffle_tracks=True,
track_density=False,
window_size=4,
hop_length=1,
time_sig=False,
velocity=False,
quantize=False,
tempo=True,
)
tokenizer = MMMTokenizer(midi, args)
got = tokenizer.tokenize_file()
# get tokens analysis
my_tokens = "PIECE_START TRACK_START ..."
MMMTokenizer.get_tokens_analytics(my_tokens)
# Convert tokens to Musa objects
MMMTokenizer.tokens_to_musa(
tokens=my_tokens,
absolute_timing=True,
time_unit="SIXTEENTH",
time_sig="4/4",
resolution=96
)
# get vocabulary
MMMTokenizer.get_vocabulary(
dataset_path="apth/to/dataset/tokens",
)- Converters
contains converters to other formats (JSON,...).
from musicaiz.loaders import Musa
from musicaiz.loaders import musa_to_proto, proto_to_musa
# Convert a musicaiz objects in protobufs
midi = Musa(midi_sample, structure="bars")
protobuf = musa_to_proto(midi)
# Convert a protobuf to musicaiz objects
musa = proto_to_musa(protobuf)
- Datasets
contains helper methods to work with MIR open-source datasets.
from musicaiz.tokenizers import MMMTokenizer, MMMTokenizerArguments
from musicaiz.datasets import JSBChorales
# Tokenize a dataset in musicaiz
output_path = "path/to/store/tokens"
args = MMMTokenizerArguments(
prev_tokens="",
windowing=True,
time_unit="HUNDRED_TWENTY_EIGHT",
num_programs=None,
shuffle_tracks=True,
track_density=False,
window_size=32,
hop_length=16,
time_sig=True,
velocity=True,
)
dataset = JSBChorales()
dataset.tokenize(
dataset_path="path/to/JSB Chorales/midifiles",
output_path=output_path,
output_file="token-sequences",
args=args,
tokenize_split="all"
)
vocab = MMMTokenizer.get_vocabulary(
dataset_path=output_path
)- Models
contains ML models to generate symbolic music.
This project is licensed under the terms of the AGPL v3 license.
To install the latest stable version run: pip install musicaiz
To install the latest version, clone this repository and run:
pip install -e .
If you want to train the models in the models submodule, you must install apex. Follow the instructions on https://github.com/NVIDIA/apex.
conda env update -f environment.yml
conda activate musicaiz
flake8 and black
Use mypy package to check variables tpyes:
mypy musicaiz
See docs.
If you use this software for your research, please cite:
@misc{hernandezolivan22musicaiz,
doi = {10.48550/ARXIV.2209.07974},
url = {https://arxiv.org/abs/2209.07974},
author = {Hernandez-Olivan, Carlos and Beltran, Jose R.},
title = {musicaiz: A Python Library for Symbolic Music Generation, Analysis and Visualization},
publisher = {arXiv},
year = {2022},
}
Musicaiz software can be extended in different ways, see some example in TODOs. If you want to contribute, please follow the guidelines in Develop