While plenty of other models are running text generation, voice synthesization, generated art, and even short video, there haven’t been many quality examples of music generation released to the public. According to the accompanyingavailable on the preprint arXiv repository, one of the main challenges with music is that it requires running the full frequency spectrum, which requires more intense sampling. That’s not to mention the complex structures and overlapping instrumentation found in music.
Though for artists, what may be most concerning about the model is its training data. According to the research paper, MusicGen was trained on 20,000 hours of licensed music from an internal dataset that includes 10,000 music tracks. In addition, the company used around 390,000 instrument-only tracks featured on Shutterstock and Pond5. The Meta researchers claimed all the music their model is trained on was “covered by legal agreements with the right holders.
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