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An AI for Automatic Instrumentation

Home Page: https://salu133445.github.io/arranger/

License: MIT License

Python 84.69% Jupyter Notebook 1.15% Shell 14.16%

arranger's Introduction

Arranger

Directory structure

├─ analysis         Notebooks for analysis
├─ scripts          Scripts for running experiments
└─ arranger         Main Python module
   ├─ config.yaml   Configuration file
   ├─ data          Code for collecting and processing data
   ├─ common        Most-common algorithm
   ├─ zone          Zone-based algorithm
   ├─ closest       Closest-pitch algorithm
   ├─ lstm          LSTM model
   └─ transformer   Transformer model

Prerequisites

You can install the dependencies by running pipenv install (recommended) or python3 setup.py install -e .. Python>3.6 is required.

Data preparation

Please follow the instruction in arranger/data/README.md.

Models

  • LSTM model
    • arranger/lstm/train.py: Train the LSTM model
    • arranger/lstm/infer.py: Infer with the LSTM model
  • Transformer model
    • arranger/transformer/train.py: Train the Transformer model
    • arranger/transformer/infer.py: Infer with the Transformer model

Baseline algorithms

  • Most-common algorithm
    • arranger/common/learn.py: Learn the most common label
    • arranger/common/infer.py: Infer with the most-common algorithm
  • Zone-based algorithm
    • arranger/zone/learn.py: Learn the optimal zone setting
    • arranger/zone/infer.py: Infer with the zone-based algorithm
  • Closest-pitch algorithm
    • arranger/closest/infer.py: Infer with the closest-pitch algorithm
  • MLP model
    • arranger/mlp/train.py: Train the MLP model
    • arranger/mlp/infer.py: Infer with the MLP model

Configuration

In arranger/config.yaml, you can configure the MIDI program numbers used for each track in the sample files generated. You can also configure the color of the generated sample piano roll visualization.

arranger's People

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