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MIDI Embeddings

Overview

This project implements a Transformer-based model for generating embeddings from MIDI files, focusing on learning meaningful representations of musical pieces.

Main Script: main.py

Purpose

The main.py script serves as the primary entry point for training and evaluating a MIDI embedding model. When a user runs this script, the following will happen:

Workflow

  1. Configuration Setup

    • A configuration dictionary is created with hyperparameters for the model and training process
    • Parameters include sequence length, embedding dimensions, attention heads, model layers, batch size, epochs, learning rate, and dropout rate

  2. Dataset Preparation

    • Loads three datasets:
      • Training dataset
      • Validation dataset
      • Test dataset
    • Uses MIDIDatasetPresaved and MIDIDatasetDynamic for efficient data handling
    • Tokenizes MIDI files using a pre-trained tokenizer if available, otherwise trains a new one

  3. Model Initialization

    • Creates a MIDITransformerEncoder with the specified configuration

  4. Model Training

    • Trains the model using the training dataset
    • Validates performance on the validation dataset
    • Saves the best-performing model checkpoint

  5. Model Evaluation

    • Evaluates the trained model on the test dataset
    • Prints out the test loss and perplexity metrics

  6. Embedding Visualization

    • Generates and saves an interactive HTML visualization of embeddings for all the songs in the MAESTRO-sustain-v2 dataset using t-SNE

Example Usage

python main.py

Installation

pip install requirements.txt

Key Components

  • transformer.py: Defines the MIDI Transformer Encoder architecture
  • dataset.py: Handles MIDI dataset loading and preprocessing
  • train.py: Contains training and evaluation functions
  • visualize.py: Provides embedding visualization functions

Customization

Users can modify the configuration dictionary in main.py to experiment with different hyperparameters, such as:

  • Embedding dimensions
  • Number of attention heads
  • Number of model layers
  • Learning rate
  • Batch size
  • Dropout rate
  • Sequence length
  • Number of epochs

Visualization

The script generates an interactive HTML visualization of song embeddings, allowing users to explore how different musical pieces are represented in the embedding space.