Music Composer is a Python-based program that employs LSTM (Long Short-Term Memory) networks to generate unique musical sequences. This tool can be used to create original music compositions or assist musicians in the creative process.
- Data Preprocessing: Load MIDI files, extract musical notes and chords, and prepare sequences for training the LSTM model.
- Model Training: Train the LSTM model using preprocessed music data to learn the patterns and structure of musical sequences.
- Music Generation: Generate new musical sequences using the trained LSTM model, allowing for the creation of original compositions.
- Evaluation: Evaluate the quality of the generated music sequences based on metrics such as pitch range and note duration.
- User Interaction: Interact with the program through a command-line interface to generate music and control the process.
The program consists of the following components and files:
- main.py: The main entry point of the program that orchestrates the training, generation, evaluation, and user interaction.
- data_preprocessing.py: Handles preprocessing of music data, including loading MIDI files and preparing sequences.
- model.py: Defines the LSTM model architecture for music generation.
- train.py: Trains the LSTM model using preprocessed music data.
- generate.py: Generates new musical sequences using the trained model.
- evaluation.py: Evaluates the quality of the generated music sequences.
- music_utils.py: Utility functions for handling music data, such as saving MIDI files and plotting music sequences.
- config.py: Configuration parameters for the model, training process, and data handling.
- dataset.py: Dataset class for loading and preprocessing music data.
- losses.py: Custom loss functions for training the LSTM model.
To use the Music Composer program:
- Install the required dependencies listed in
requirements.txt
. - Prepare your music dataset in MIDI format and specify the data path in
config.py
. - Run
main.py
to train the model, generate music, and interact with the program.
- Implement more sophisticated evaluation metrics for assessing the musicality of generated sequences.
- Explore advanced LSTM architectures and hyperparameter tuning for improved music generation.
- Integrate with external libraries or APIs for additional features such as music playback and visualization.