Fine-tune MusicGen to generate music in any style

Replicate's guide on fine-tuning MusicGen offers a detailed process for customizing the model to generate music in a specific style, leveraging Meta's AudioCraft and the built-in trainer Dora. The process, developed by Jongmin Jung, involves preparing a dataset of at least 9-10 tracks, each longer than 30 seconds, and includes features like automatic audio chunking, auto-labeling, and optional vocal removal to improve the output quality. Users can choose between different model sizes—small, medium, or melody—each with distinct capabilities, and must use their Replicate API token to initiate the training. After setting up a model on Replicate, users upload their training data and run the training process using Python or the Replicate CLI, with the option to monitor progress and adjust training parameters for optimal results. Once trained, the model can be accessed via web or API, allowing users to generate music by reusing training descriptions or creating new prompts to achieve the desired musical style.