Meta’s MusicGen and the Rise of AI in Music: A closer look at the latest AI model, its potential impact on the music industry, and the implications for artists, copyright, and innovation
BeatCurry Team
Meta, a notable player in the technology landscape, has recently unveiled MusicGen, a revolutionary AI model with the ability to generate music. Described as a “simple and controllable music generation LM with textual and melodic conditioning,” MusicGen allows users to input text descriptions of desired music and produces 12-second samples accordingly. By offering prompts like “an 80s driving pop song with heavy drums and synth pads in the background,” or “lofi slow bpm electro chill with organic samples,” MusicGen showcases its capabilities in diverse genres.
One significant question that arises for rightsholders is the training process behind MusicGen. According to Meta’s accompanying academic paper, the model was trained using 20,000 hours of licensed music, including a rich internal dataset of 10,000 high-quality tracks, as well as data from popular stock-music libraries like ShutterStock and Pond5.
Meta’s entry into the AI-generated music space aligns with the endeavors of other technology giants. Alphabet recently introduced its own music language model called MusicLM, which was trained on an extensive collection of approximately 280,000 hours of material sourced from the Free Music Archive. OpenAI, the renowned creator of ChatGPT and DALL-E, has also contributed to the field with MuseNet in 2019 and Jukebox in 2020.
It is worth noting that the CEOs of these companies have acknowledged the experimental nature of their music-focused AI models. Sam Altman, CEO of OpenAI, emphasized that Jukebox was primarily a research release, aiming to demonstrate the possibilities rather than serving as a widely adopted tool.
As of now, the AI music models developed by Meta, Alphabet, and OpenAI remain research projects rather than commercial products. These endeavors are expected to lay the foundation for startups and developers to explore the potential of AI-generated music. The simultaneous releases of music language models by Alphabet and Meta hold significance, driving advancements in this technology.
Amidst these exciting developments, the music industry is grappling with the implications for rights holders, artists, and copyright laws. Understanding the impact of these systems and observing how other developers leverage them will be crucial in navigating this evolving landscape.
In conclusion, Meta’s MusicGen and other AI music models are pushing the boundaries of music generation. While they are currently research-oriented rather than commercially focused, these innovations have the potential to shape the future of music creation and necessitate ongoing scrutiny by industry stakeholders.
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