musib 0.0.3

Python library for building and evaluation/benchmarking of Automated Music Generation models

MUSIB: Music Inpainting Benchmark

Python library for building and evaluation/benchmarking of Automated Music Generation models

Models

Model	Year	Repo	Paper
DeepBach	2017	Repo	Paper
CocoNet	2017	Repo	Paper
AnticipationRNN	2018	Repo	Paper
InpaintNet	2019	Repo	Paper
Music SketchNet	2020	Repo	Paper
Variable Length Infilling	2021	Repo	Paper

Datasets

Dataset	Size	Description	Source	Paper	Type
AILabs	1747	Live Piano Performances	Source	Paper	Single Instrument Polyphony
JSB Chorales	385	Bach Chorales Scores	Source	-	Fixed Voices Polyphony
IrishFolk	45849	Irish Folk Songs	Source	Paper	Monophony

Data Representation

Music SketchNet

DEFAULT_FRACTION: 24

# 0-127 note, 128 hold, 129 rest
note_seq: [
    [48, 128, 128, 128, 128, 128, 50, 128, 128, 128, 128, 128, 52, 128, 128, 128, 128, 128, 53, 128, 128, 128, 128, 128]
]

# [px, rx, len_x, nrx, gd]
factorized: [
    [48, 50, 52, 53, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128],
    [1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2],
    [4],
    [[1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0]],
    [48, 128, 128, 128, 128, 128, 50, 128, 128, 128, 128, 128, 52, 128, 128, 128, 128, 128, 53, 128, 128, 128, 128, 128]
]

model_input: [n_batch, **REVISAR**]
model_output: [n_batch, n_measures_middle, DEFAULT_FRACTION, n_classes]

DeepBach

index2note:
    {0: 'D#5', 1: 'E-4', 2: 'E-5', 3: 'rest', 4: 'F#4', 5: 'E#5', 6: 'G#4', 7: 'B4', 8: 'D4', 9: 'A5', 10: 'END', 11: 'G-4', 12: 'C#5', 13: 'G4', 14: 'A3', 15: 'D#4', 16: 'START', 17: 'D5', 18: 'C5', 19: 'F5', 20: 'A-4', 21: 'C4', 22: 'C#4', 23: 'E5', 24: 'E#4', 25: 'A#4', 26: 'D-5', 27: 'E4', 28: 'G-5', 29: 'A-5', 30: 'A4', 31: 'G5', 32: 'B-4', 33: 'F#5', 34: '__', 35: 'F4', 36: 'OOR', 37: 'G#5', 38: 'B3'}

score_tensor = tensor([[36, 34, 34, 34, 36, 34, 34, 34, 34, 34, 34, 34,  8, 34, 34, 34, 38, 34,
         34, 34, 34, 34, 14, 34, 36, 34, 34, 34, 36, 34, 34, 34, 34, 34, 14, 34,
         38, 34, 34, 34, 14, 34, 34, 34, 34, 34, 34, 34, 38, 34, 34, 34,  8, 34,
         34, 34, 34, 34, 34, 34, 21, 34, 34, 34, 38, 34, 34, 34, 14, 34, 34, 34,
         34, 34, 34, 34, 36, 34, 34, 34, 34, 34, 34, 34, 38, 34, 34, 34, 38, 34,
         34, 34, 21, 34, 34, 34,  8, 34, 34, 34,  8, 34, 34, 34, 34, 34, 21, 34,
         38, 34, 34, 34, 14, 34, 34, 34, 34, 34, 34, 34, 36, 34, 34, 34, 38, 34,
         34, 34, 34, 34, 34, 34, 21, 34, 34, 34,  8, 34, 34, 34, 34, 34, 34, 34,
         21, 34, 34, 34, 38, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 36, 34,
         34, 34, 34, 34, 34, 34, 38, 34, 34, 34,  8, 34, 34, 34, 34, 34, 34, 34,
         21, 34, 34, 34, 38, 34, 34, 34, 34, 34, 34, 34, 14, 34, 34, 34, 36, 34,
         34, 34, 34, 34, 14, 34, 38, 34, 34, 34, 14, 34, 34, 34, 34, 34, 34, 34,
         38, 34, 34, 34,  8, 34, 34, 34, 34, 34, 34, 34, 21, 34, 34, 34, 38, 34,
         34, 34, 14, 34, 34, 34, 34, 34, 34, 34, 36, 34, 34, 34, 34, 34, 34, 34]])

# Metadata = [Fermata, Tick, Key, N_Voice]
metadata_tensor = tensor([[ 0,  0, 15,  0],
        [ 0,  1, 15,  0],
        [ 0,  2, 15,  0],
        [ 0,  3, 15,  0],
        [ 0,  0, 15,  0],
        [ 0,  1, 15,  0],
        [ 0,  3, 15,  0],
        [ 0,  0, 15,  0],
        [ 1,  2, 15,  0],
        [ 1,  3, 15,  0],
        [ 1,  0, 15,  0],
        [ 1,  1, 15,  0],
        [ 1,  2, 15,  0],
        [ 1,  3, 15,  0]])

---

Project Organization

├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── interim        <- CSV data containing all notes coming from raw sources. Intermediate before vectorization.
│   ├── processed      <- Vectorization of data ready to feed the models.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained models weights.
│
├── results            <- Generated results, tables, csvs, etc.
│   └── images         <- Generated graphics and figures
│
├── environment.yaml   <- Libraries and modules required by the environment to reproduce the project.
│
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    ├── data           <- Scripts to download or generate data
    │   ├── download_from_souce.py
    │   ├── standardize_data.py
    │   └── process_data.py
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
    ├── models         <- Scripts to train models and generate new data.
    │   ├── sketchnet.py
    │   ├── inpaintnet.py
    │   ├── arnn.py
    │   └── vli.py
    │
    ├── metrics         <- Scripts to calculation of metrics.
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations
        └── plot_metrics.py

---

Project based on the cookiecutter data science project template.