musecoco-text2midi-service

🎵 MuseCoco Text-to-MIDI Service

The MuseCoco Text-to-MIDI Service is a refactored version of the MuseCoco repository, designed as a deployable service module. This service adapts to new data, manages the history of its checkpoints, and abstracts away the underlying implementation details to provide a seamless interface for generating MIDI files from textual inputs. Detailed comments are included to facilitate easy navigation and understanding of the codebase.

📋 Table of Contents

✨ Features

📂 Directory Structure

The repository is organized as follows:

musecoco-text2midi-service/
├── src/
│   └── musecoco_text2midi_service/
│       ├── control/                   # Controllers for orchestrating service logic
│       │   ├── __init__.py
│       │   ├── _musecoco/
│       │   │   ├── __init__.py
│       │   │   └── view.py
│       │   └── _text2midi.py
│       ├── dao/                       # Data Access Objects for configuration management
│       │   ├── __init__.py
│       │   └── _config_manager.py
│       ├── model/                     # Models representing the structure and workflow of MIDI generation
│       │   ├── __init__.py
│       │   └── _config_model.py
│       ├── utils/                     # Utility functions for common tasks
│       │   ├── __init__.py
│       │   └── _watch_dog.py
│       └── view/                      # Views for API or CLI outputs
│           ├── __init__.py
│           └── _app_view.py
├── storage/
│   ├── checkpoints/                   # Model checkpoints
│   │   └── linear_mask-1billion/
│   │       ├── checkpoint_2_280000.pt
│   │       └── README.md              # Instructions for managing checkpoints
│   ├── config/                        # Configuration files
│   │   ├── main_config.yaml           # Main configuration file
│   │   ├── att_key.json
│   │   └── num_labels.json
│   ├── input/                         # Input files for predictions
│   │   ├── predict_backup.json        # Example input format for predictions
│   │   └── predict.json
│   ├── log/                           # Log files
│   └── tmp/                           # Temporary files and outputs
├── tests/
│   ├── __init__.py                    # Initializer for the tests package
│   └── ...                            # Test modules for various components
├── .gitignore                         # Specifies files and directories to ignore in version control
├── LICENSE                            # License file
├── main.py                            # Entry point for running the service
├── README.md                          # Project description and instructions
└── setup.py                           # Setup script for packaging and distribution

⚙️ Installation

To install the MuseCoco Text-to-MIDI Service, follow these steps:

  1. Clone the Repository:

    git clone https://github.com/your-repo/musecoco-text2midi-service.git
cd musecoco-text2midi-service

  2. Install Dependencies:

    Create a new environment and install the dependencies. Currently, PyTorch needs to be installed with conda:

    conda env create -f conda_env.yml
conda activate MuseCoco
conda -c nvidia/label/cuda-12.3
pip install torch==2.3
pip install --user --no-cache-dir pytorch-fast-transformers
pip install -e .

<!-- cd modules
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install -e . -->

    Note: Other dependencies can be installed with pip install -e . or pip install musecoco_text2midi_service. PyTorch installation with pip will be supported later.

🔧 Configuration

The service uses YAML configuration files located in storage/config/. The main configuration file is main_config.yaml, which is used by the main.py script. You can modify these files to configure parameters such as model checkpoints, logging settings, and API keys.

Checkpoints should follow the instructions provided in storage/checkpoints/linear_mask-1billion/README.md and be saved in the same directory as the README.md file.

🚀 Usage

To start the service, run:

python main.py

The main.py file provides a terminal-based app demo.

Refer to storage/input/predict_backup.json for examples of acceptable input formats for the service. This file contains sample data that illustrates how to structure text input for the MIDI generation process.

You can also import the package to your project.

from musecoco_text2midi_service.control import Text2Midi
from musecoco_text2midi_service.dao import load_config_from_file

config = load_config_from_file("storage/config/main_config.yaml")
text2midi = Text2Midi(config)

input_text = "This music's use of major key creates a distinct atmosphere, with a playtime of 1 ~ 15 seconds. The rhythm in this song is very pronounced, and the music is enriched by grand piano, cello and drum. Overall, the song's length is around about 6 bars. The music conveys edginess."

midi_data, meta_data = text2midi.text_to_midi(input_text, return_midi=True)

Later this will launch the MuseCoco Text-to-MIDI Service, allowing you to convert textual descriptions into MIDI files through the defined APIs or CLI.

🧪 Running Tests

To run the test suite, use:

pytest /tests

This command will execute all test cases in the tests directory and provide a report of the test results. Ensure that the project is built correctly before running the tests.

🤝 Contributing

We welcome contributions from the community. Please follow these steps to contribute:

  1. Fork the repository.
  2. Create a new branch for your feature or bugfix.
  3. Commit your changes with descriptive commit messages.
  4. Push your changes to your forked repository.
  5. Create a pull request with a detailed description of your changes.

📄 License

This project is licensed under Apache License 2.0 - see the LICENSE file for more details.

Reference:

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Notice

Thank you for your interest in the MuseCoco Text-to-MIDI Service! If you have any questions or need further assistance, feel free to open an issue or contact us.

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