What is Seed-Music
Seed-Music is anAI music generationlarge model launched by ByteDance, which transforms 10-second audio recordings made by users into complete musical works. Through autoregressive language models and diffusion methods, it generates high-quality, style-controllable music based on users' multimodal inputs such as style descriptions, audio references, sheet music, and voice prompts. Seed-Music aims to simplify the music creation process, allowing both beginners and professional musicians to easily create music. It not only generates complete audio works, but also provides music editing features, enabling users to make personalized adjustments to the generated music.
Main Features of Seed-Music
- Lyrics and melody editing: Users can directly edit lyrics and melodies in the generated audio to achieve personalized music creation.
- Zero-shot singing voice conversion: Supports users by providing 10 seconds of singing or ordinary speech, and Seed-Music converts the user's voice into an expressive singing performance, supporting imitation of songs of any gender and style.
- Symbolic music representation: Seed-Music introduces "lead sheet tokens" as a symbolic music representation, allowing users to understand and edit music in a more intuitive way, including melody, harmony, and rhythm, etc.
- Music structure editing: Users can edit different parts of the music, such as verses, choruses, and other structural elements, to adapt to specific creative needs.
- Music style and emotion adjustment: Seed-Music supports users in adjusting the style and emotion of the generated music to match their creative vision.
Technical Principles of Seed-Music
- Autoregressive Language Model (LM): Predicts the next element in a music sequence, such as notes, rhythms, or chords, by learning patterns in music datasets. In music generation, the autoregressive model generates coherent music sequences based on given inputs such as lyrics, melody fragments, or other musical features.
- Diffusion Models: Generate data by gradually removing noise, similar to the diffusion phenomenon in physical processes. In music editing, diffusion models can be used to finely adjust musical elements, such as modifying melody or harmony, while maintaining the natural smoothness of the music.
- Zero-Shot Learning: In Seed-Music, zero-shot singing voice conversion allows users to convert their own voice into a specific singing style without providing a large number of samples.
- Multimodal input processing: The system can process and understand multiple types of input data, such as text, audio, and sheet music, and fuse these data to generate music.
- Note-Level Editing: The system provides fine control over music, allowing users to edit at the note level, including modifying pitch, duration, and velocity, etc.
Seed-Music 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 creation: Music enthusiasts use Seed-Music to create their own songs without needing deep music theory knowledge or performance skills.
- Professional music production: Music producers and composers use Seed-Music to generate music demos, quickly prototype, or as a source of creative inspiration.
- Music education: Teachers and students use Seed-Music as a teaching tool to learn music theory and composition techniques through practice.
- Social media content creation: Content creators generate unique background music for their social media posts, enhancing the appeal of visual content.
- Advertising and multimedia production: Advertisers and multimedia producers generate customized music and soundtracks for commercials, videos, films, and games.