In the context of AI music, inference refers to the process of generating music from a trained AI model based on input data or prompts. It is the stage where the AI takes learned patterns from its training data and applies them in real-time to create melodies, harmonies, rhythms, or even full compositions.

For example, an AI model trained on thousands of classical piano pieces can, during inference, generate a brand-new composition in the style of Beethoven based on a simple input like “create a dramatic piano sonata in C minor.” This process is different from training, which involves feeding the model vast amounts of musical data to learn styles, structures, and patterns.

In AI music, inference can happen in real-time (as in interactive AI-generated accompaniment) or in batch mode (where a full composition is created before playback). The quality and creativity of the output depend on the model’s architecture, training data, and prompt specificity. AI music tools like OpenAI’s MuseNet, Google’s MusicLM, and Meta’s AudioCraft all rely on inference to transform user inputs into coherent musical pieces.