MUSIB: Music Inpainting Benchmark

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

Models

Datasets

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.