Models by this creator




Total Score


MusicLang is an AI model developed by the team at musiclang that can generate controllable symbolic music. It is trained on musical data to understand and generate music conditioned on various inputs like chord progressions, melodies, and more. This model can be contrasted with similar music generation models like ChatMusician and the MusicGen series, which have different architectures and training approaches. Model inputs and outputs MusicLang takes in various musical parameters as inputs to generate music. These include: Inputs Chord Progression**: A sequence of chords that the generated music should follow Time Signature**: The time signature of the generated music Number of Tokens**: The number of musical tokens to generate Outputs Musical Score**: The generated musical score, which can be exported to a MIDI file for further processing or playback in a digital audio workstation. Capabilities MusicLang can generate high-quality symbolic music that adheres to the provided musical inputs. It is capable of producing full-length music pieces with coherent chord progressions and melodies. The model can be used to quickly generate creative musical ideas or to continue an existing piece with a specified harmonic structure. What can I use it for? MusicLang can be useful for music composers, producers, and enthusiasts who want to rapidly generate musical ideas or explore different chord progressions and harmonic structures. The model's ability to export the generated music to MIDI makes it easy to further refine and elaborate on the output in a digital audio workstation. Additionally, the model's speed and ability to run on laptops without a GPU make it accessible for a wide range of users. Things to try One interesting thing to try with MusicLang is to provide it with a specific chord progression and see how it generates music that fits within that harmonic framework. You can also experiment with adjusting the temperature and top-p parameters to control the diversity and creativity of the generated output. Additionally, trying out different seed values can produce varied results while keeping the overall musical structure consistent.

Read more

Updated 6/13/2024