Deep neural network for generating 4-minute musical compositions with 10 instruments and style blending.
MuseNet is a deep neural network developed by OpenAI, utilizing a large-scale transformer architecture—specifically a 72-layer sparse transformer—to generate musical compositions in MIDI format. While originally launched as a research prototype in 2019, its technical significance in 2026 remains a foundational benchmark for tokenizing polyphonic music. The model was trained on hundreds of thousands of MIDI files across diverse genres, allowing it to maintain long-term structural consistency over 4-minute durations. Unlike traditional audio generators, MuseNet operates in the symbolic domain (MIDI), enabling it to understand and manipulate discrete musical elements such as harmony, rhythm, and instrumentation. In the 2026 landscape, while OpenAI has shifted focus toward diffusion-based audio models (like Jukebox and Sora-Audio), MuseNet persists as a specialized tool for composers needing structured, editable MIDI data rather than raw waveforms. It supports 10 different instruments and can blend styles that are traditionally disparate, such as Chopin and Lady Gaga, by learning the underlying patterns of musical 'intent' rather than just acoustic frequency.
Utilizes a 72-layer network with sparse attention patterns to manage long-range dependencies in musical sequences.
The premier AI music composition engine for unique, emotional soundtracks and MIDI-level creative control.
Architecting studio-grade MIDI and audio compositions through advanced algorithmic music theory.
Cloud-native DAW with integrated AI-driven orchestration and stem isolation.
AI-powered songwriting assistant for data-driven melody and chord progression generation.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Encodes pitch, volume, and instrument data into discrete tokens that the model predicts sequentially.
The model uses learned embeddings to interpolate between musical genres during the generation process.
Accepts external MIDI input to serve as the initial context for the transformer's prediction window.
Applies the characteristics of one composer to a different melody without explicit retraining.
Allows the user to force or restrict specific MIDI channels during the generation phase.
Visual representation of how the transformer is weighing different musical notes in its attention heads.
Quickly generating thematic variations for different game levels.
Registry Updated:2/7/2026
Demonstrating how different composers handle counterpoint.
Overcoming writer's block for songwriters.