![]() ![]() The AudioCraft family of models are capable of producing high-quality audio with long-term consistency, and they’re easy to use. Music is arguably the most challenging type of audio to generate as it’s composed of local and long-range patterns, from a suite of notes to a global musical structure with multiple instruments. Generating high-fidelity audio of any kind requires modeling complex signals and patterns at varying scales. There’s some work out there, but it’s highly complicated and not very open, so people aren’t able to readily play with it. While we’ve seen a lot of excitement around generative AI for images, video, and text, audio has seemed to lag a bit behind. We’re open-sourcing these models, giving researchers and practitioners access so they can train their own models with their own datasets for the first time, and help advance the field of AI-generated audio and music. And lastly, we’re sharing all of the AudioCraft model weights and code. We’re also releasing our pre-trained AudioGen models, which let you generate environmental sounds and sound effects like a dog barking, cars honking, or footsteps on a wooden floor. Today, we’re excited to release an improved version of our EnCodec decoder, which allows higher quality music generation with fewer artifacts. MusicGen, which was trained with Meta-owned and specifically licensed music, generates music from text prompts, while AudioGen, which was trained on public sound effects, generates audio from text prompts. That’s the promise of AudioCraft - our latest AI tool that generates high-quality, realistic audio and music from text.ĪudioCraft consists of three models: MusicGen, AudioGen and EnCodec. Or a small business owner adding a soundtrack to their latest video ad on Instagram with ease. ![]() Imagine a professional musician being able to explore new compositions without having to play a single note on an instrument. ![]()
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