Seed-Music - ByteDance's AI music generation model
Seed-Music is an AI music generation model launched by ByteDance that transforms 10-second audio recordings by users into complete musical works. It utilizes an autoregressive language model and diffusion methods, based on the user's multimodal input (such as style...).
What is Seed-Music?
Seed-Music is an AI-powered music generation model launched by ByteDance, transforming 10-second audio recordings by users into complete musical works. Through an autoregressive language model and diffusion methods, it generates high-quality, style-controlled music based on the user's multimodal input (such as style descriptions, audio references, sheet music, and sound cues). Seed-Music aims to simplify the music creation process, allowing both beginners and professional musicians to easily create music. In addition to generating complete audio works, it also provides music editing functions, allowing users to personalize the generated music.
Seed-Music's main functions
- Lyrics and melody editingUsers can directly edit lyrics and melodies in the generated audio to achieve personalized music creation.
- Zero-sample singing conversionSeed-Music allows users to provide 10 seconds of vocals or plain speech, transforming their voices into expressive singing performances that can mimic songs of any gender and style.
- Symbolic music representationSeed-Music introduces "lead sheet tokens" as symbolic representations of music, allowing users to understand and edit music in a more intuitive way, including melody, harmony, and rhythm.
- Musical Structure EditingUsers can edit different parts of the music, such as verses, choruses, and other structural elements, to suit specific creative needs.
- Musical style and emotional adjustmentSeed-Music allows users to adjust the style and emotion of generated music to match their creative vision.
The technical principles of Seed-Music
- Auto-regressive Language Model (LM)Autoregressive models predict the next element in a musical sequence, such as a note, rhythm, or chord, by learning patterns from a music dataset. In music generation, autoregressive models generate coherent musical sequences based on a given input, such as lyrics, melodic fragments, or other musical features.
- Diffusion ModelsThis model generates data by progressively removing noise, similar to the diffusion phenomenon in physical processes. In music editing, diffusion models can be used to fine-tune musical elements, such as modifying melody or harmony, while maintaining the natural flow of the music.
- Zero-Shot LearningIn Seed-Music, zero-sample vocal conversion allows users to transform their voice into a specific vocal style without providing a large number of samples.
- Multimodal input processingThe system can process and understand various types of input data, such as text, audio, and sheet music, and merge these data to generate music.
- Note-Level EditingThe system provides fine-grained control over music, allowing users to edit at the note level, including modifying pitch, duration, and dynamics.
Seed-Music's project address
- Project official website:team.doubao.com/en/special/seed-music
- arXiv technical paper:https://arxiv.org/pdf/2409.09214
Seed-Music Application Scenarios
- Personal music creationMusic enthusiasts use Seed-Music to create their own songs without needing extensive music theory knowledge or performance skills.
- Professional music productionMusic producers and composers use Seed-Music to generate music demos, quickly prototype, or as a source of creative inspiration.
- Music EducationTeachers and students use Seed-Music as a teaching tool to learn music theory and composition skills through practice.
- Social media content creationContent creators generate unique background music for their social media posts to enhance the appeal of their visual content.
- Advertising and multimedia productionAdvertisers and multimedia producers generate custom music and soundtracks for commercials, videos, movies, and games.