https://github.com/johnlk/chopinai

This project solicits the help of the LSTM neural network to generate a unique music file based on previous works by Chopin.
https://github.com/johnlk/chopinai

Last synced: about 1 year ago
JSON representation

This project solicits the help of the LSTM neural network to generate a unique music file based on previous works by Chopin.

• Host: GitHub
• URL: https://github.com/johnlk/chopinai
• Owner: johnlk
• Created: 2017-03-23T04:30:58.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2024-12-22T18:24:57.000Z (over 1 year ago)
• Last Synced: 2025-03-31T19:21:13.604Z (about 1 year ago)
• Language: C
• Homepage:
• Size: 365 MB
• Stars: 7
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Computer Generated Chopin Music

This project uses deep learning to generate classical piano music in the style of Chopin through a novel text-based approach. This project converts MIDI files into text representations that can be easily processed by RNNs.

Best output found here: `output/2024-pytorch-rnn/temp-0.8/tmp.mid`

## Project History & Evolution

### 2017 Version

The project originally used Andrej Karpathy's char-rnn implementation for generating music. This version produced interesting results (preserved in `output/2017-karpathy-LSTM/`) but relied on external dependencies and older deep learning frameworks.

### 2024 Update

After 7 years, the project was modernized with a custom PyTorch-based implementation. The new version features:

- Simplified architecture using PyTorch's built-in LSTM modules

- More efficient training pipeline

- Better code organization and documentation

- Improved generation quality through modern deep learning practices

## Technical Overview

### Architecture

The current implementation uses a character-level RNN built with PyTorch, consisting of:

- Embedding layer for character encoding

- Multi-layer LSTM network

- Dropout regularization for preventing overfitting

- Linear output layer for character prediction

- Mini-batch training with CUDA GPU support

### Data Processing Pipeline

1. **MIDI Collection**: Source MIDI files are stored in `data/midi/`

2. **Text Conversion**: MIDI files are converted to ASCII format using conversion tools in `src/conversion/`

3. **Data Preprocessing**: ASCII files are stored in `data/ascii/` and processed into training data

4. **Training**: The PyTorch model is trained on the text representation

5. **Generation**: New music is sampled from the model at different temperature settings

### Model Parameters

- Training options:

  - Hidden layer size: 256 (configurable)

  - Number of LSTM layers: 2 (configurable)

  - Dropout rate: 0.2

  - Batch size: 128

  - Sequence length: 100

- Temperature settings tested: 0.4, 0.8, 1.0

  - Higher temperatures produce more creative but potentially error-prone compositions

  - Lower temperatures generate more conservative, structured pieces

## Generated Outputs

The project maintains both historical and current outputs:

- `output/2018-karpathy-LSTM/`: Original char-rnn generated pieces

- `output/2024-pytorch-rnn/`: New PyTorch model generations

  - `temp-0.4/`: Conservative generation

  - `temp-0.8/`: Balanced creativity/structure

  - `temp-1.0/`: Most experimental generation

The MIDI files can be played using standard music software like GarageBand or this [web player](https://midiplayer.ehubsoft.net/).

## Technical Roadmap

1. Extended Training: Increase training duration and dataset size

2. Architecture Tuning:

   - Experiment with larger hidden layer sizes

   - Test different dropout rates

   - Try additional LSTM layers

3. Data Augmentation: Add more Chopin compositions to training set

4. Evaluation Metrics: Implement quantitative measures of musical quality

## Implementation Details

The core implementation is in `src/model/`, featuring:

- `train.py`: Model definition and training logic

- `generate.py`: Text generation utilities

- Efficient batch processing

- CUDA optimization for GPU acceleration

- Configurable sequence length for capturing musical patterns

- Checkpoint saving/loading for iterative training

For detailed usage instructions and parameters, see the model documentation in `src/model/README.md`.