MusicLM

Meta's MusicGen is an open source, deep-learning language model that can generate short pieces of music based on text prompts. The model was trained on 20,000 hours of music, including whole tracks and individual instrument samples. The model will generate 12 seconds of audio based on the description you provided. You can optionally provide reference audio from which a broad melody will be extracted. The model will then try to follow both the description and melody provided. All samples are generated with the melody model. You can also use your own GPU or a Google Colab by following the instructions on our repo. MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models. MusicGen can generate high-quality samples, while being conditioned on textual description or melodic features, allowing better control over the generated output.

Happy Shrimp 1.0 is an AI music generation model designed to turn vague ideas into fully produced songs from a single prompt. Users can start with an emotion, story concept, target genre, or creative direction, without needing technical music knowledge such as BPM, key signature, or instrumentation. The model supports text-to-music generation for complete vocal songs and instrumental compositions, producing melody, arrangement, lyrics, and vocals from scratch or using lyrics supplied by the user. Built with broad musical knowledge, it understands creative aesthetics across genres, regions, eras, and cultural contexts, translating descriptive prompts into structured musical outputs. It performs across styles including Chinese music, pop, R&B and soul, hip hop, rock, funk, electronic, classical, and jazz. Using world knowledge and music-domain reasoning, the model represents both the “grammar” of music.