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Drill dataset for AI-Generated Music

Training machine learning models on the Drill Music Dataset allows researchers and developers to explore further into the genre's complexities. The intense rhythmic content and distinct production components present models with a challenge in understanding and replicating the subtle patterns inherent in drill music.

Each professionally curated audio file in this dataset has rich metadata, offering a wealth of information for machine learning training. Chords, instrumentation, key, tempo, timestamps, and other pertinent information are all included in the metadata. This extensive quantity of data enables a variety of use cases, making it an invaluable asset for generative AI music, source separation, and other cutting-edge applications.

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Dataset Highlights



Each musical composition in the dataset is accompanied by detailed metadata, offering information about key signatures, tempo, time signatures, and other relevant musical attributes. This metadata enhances the dataset's usability for both training and analysis.

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Varied Lengths and Complexity

To accommodate a range of applications, the dataset includes musical pieces of varying lengths and complexity. This diversity facilitates the development of AI models capable of generating drill music with different durations and intricacies.


Rich Musical Content

Drill Dataset encompasses a wide array of musical elements, including drum patterns, basslines, melodies, and harmonic structures, ensuring a rich and varied training environment for AI algorithms.


Genre Specificity

The dataset exclusively concentrates on the drill music genre, providing a diverse range of drill compositions for training AI models to capture the unique characteristics, rhythms, and instrumentation associated with this genre.


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