suhanpark / muzik.ai

Getting Started

Prerequisites:

• Google Cloud Platform account
• Python 3.7+
• Virtual environment (recommended)

Installation:

1. Clone the repository: git clone https://github.com/suhanpark/muzik.ai.git
2. Navigate to the project directory: cd muzik.ai
3. Install dependencies: pip install -r requirements.txt

Configuration:

1. Set up Google Cloud Storage buckets for your data and models.
2. Update the configuration files in the data_pipeline and model directories with your GCP credentials and bucket information.

Running the Pipeline:

1. Data Ingestion: python data_pipeline/data_ingestion.py
2. Data Preprocessing: python data_pipeline/data_preprocessing.py
3. Model Training: python model/train.py
4. Music Generation: python model/generate.py

Note: This project is currently under active development. I'm continuously working on enhancing the MLOps pipeline and expanding the capabilities of our music generation system.

Future Directions

• Real-time Music Generation: Implement a system for generating music in real-time, potentially integrating with live performance setups.
• User-Controlled Parameters: Allow users to specify desired musical attributes, such as genre, mood, and instrumentation, to guide the generation process.
• Model Optimization: Explore techniques for optimizing model size and inference speed to enable deployment on resource-constrained devices.

Contributing

We welcome contributions from the open-source community! If you're passionate about AI music generation and want to contribute to this project, please refer to our CONTRIBUTING.md file for guidelines.

License

This project is licensed under the MIT License.