Takeaways
- AudioCraft simplifies the process of producing high-quality audio and music from text prompts.
- We are making MusicGen, AudioGen, and EnCodec open-source for research purposes, aiming to contribute to the progress of AI-generated audio.
Envision a skilled musician diving into new musical compositions without the need to physically play an instrument. Alternatively, picture a small business proprietor effortlessly adding an engaging soundtrack to their latest Instagram video advertisement. This is the potential offered by AudioCraft, our groundbreaking AI tool designed to create realistic and high-quality audio and music from text.
AudioCraft comprises three distinct models: MusicGen, AudioGen, and EnCodec. MusicGen, trained on licensed music from Meta, is capable of generating music based on text prompts. On the other hand, AudioGen, trained in publicly available sound effects, generates audio from text prompts. Today, we are thrilled to introduce an enhanced version of our EnCodec decoder, which facilitates superior music generation with fewer imperfections. Additionally, we're unveiling our pre-trained AudioGen models, empowering you to generate environmental sounds and effects such as a dog's bark, car horns, or footsteps on a wooden surface. Lastly, we are sharing the complete AudioCraft model weights and code for your exploration.
We're taking a step towards open-sourcing these models, granting researchers and practitioners the access they need to train their own models using their unique datasets. This marks the first time such an opportunity is available, contributing to the advancement of AI-generated audio and music.
While generative AI has made significant strides in images, video, and text, audio has somewhat lagged behind. While some efforts exist, they tend to be intricate and not very accessible, limiting experimentation. Crafting high-quality audio of various types requires understanding intricate signals and patterns across different scales. Music, in particular, poses significant challenges in generating, given its blend of local and global patterns, from individual notes to the overall musical structure with multiple instruments.
The AudioCraft models are an encompassing solution, capable of producing top-notch audio consistently over long durations, all while being user-friendly. With AudioCraft, we've simplified the design of generative audio models compared to previous work in the field. We're providing the complete recipe for utilizing the existing models that Meta has been refining for years, empowering users to explore the boundaries and forge their own models.
AudioCraft seamlessly accommodates music, sound, compression, and generation, all under one roof. This adaptability facilitates building and reusing, enabling individuals interested in enhancing sound generators, compression algorithms, or music generators to leverage a unified codebase, building upon others' accomplishments.
A robust open-source foundation will foster innovation and complement the future landscape of audio and music creation and consumption. We believe that with enhanced controls, MusicGen has the potential to transform into a novel type of instrument, much like the introduction of synthesizers.
We view the AudioCraft models as tools that inspire musicians and sound designers, facilitating quick brainstorming and iterations in novel ways. The prospects of what individuals can achieve with AudioCraft are boundless, and we're eagerly looking forward to witnessing the creations that emerge.
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