What is Seed-Music?

Seed-Music is a large AI music generation model developed by ByteDance, designed to convert a 10-second audio clip recorded by users into a full music composition. It utilizes autoregressive language models and diffusion methods to generate high-quality, style-controllable music based on multimodal inputs such as style descriptions, audio references, sheet music, and sound cues. Seed-Music aims to simplify the music creation process, making it accessible for both beginners and professional musicians. In addition to generating complete audio works, it also offers music editing features, allowing users to personalize the generated music.

Key Features of Seed-Music

• Lyrics and Melody Editing: Users can directly edit lyrics and melodies within the generated audio, enabling personalized music creation.
• Zero-Shot Singing Voice Conversion: Seed-Music can transform a user's voice from a 10-second singing or speech sample into an expressive singing performance, capable of mimicking any gender or style.
• Symbolic Music Representation: Seed-Music introduces "lead sheet tokens" as a symbolic music representation, allowing users to understand and edit music more intuitively, including melody, harmony, and rhythm.
• Music Structure Editing: Users can edit different parts of the music, such as verses, choruses, and other structural elements, to suit specific creative needs.
• Music Style and Emotion Adjustment: Seed-Music allows users to adjust the style and emotion of the generated music to match their creative vision.

Technical Principles of Seed-Music

• Auto-regressive Language Model (LM): Predicts the next element in a music sequence, such as a note, rhythm, or chord, by learning patterns from music datasets. In music generation, the auto-regressive model generates coherent music sequences based on given inputs like lyrics, melody fragments, or other musical features.
• Diffusion Models: Generates data by gradually removing noise, similar to the diffusion process in physics. In music editing, diffusion models can finely adjust musical elements like melody or harmony while maintaining natural fluidity.
• Zero-Shot Learning: Allows users to convert their voice into a specific singing style without providing extensive samples.
• Multimodal Input Processing: The system can process and understand various types of input data, such as text, audio, and sheet music, and fuse these data to generate music.
• Note-Level Editing: Provides fine-grained control over music, allowing users to edit at the note level, including modifying pitch, duration, and intensity.

Application Scenarios of Seed-Music

• Personal Music Creation: Music enthusiasts can use Seed-Music to create their own songs without needing deep music theory knowledge or performance skills.
• Professional Music Production: Music producers and composers can use Seed-Music to generate music demos, quickly prototype ideas, or use it as a source of creative inspiration.
• Music Education: Teachers and students can use Seed-Music as a teaching tool to learn music theory and composition techniques through practice.
• Social Media Content Creation: Content creators can generate unique background music for their social media posts, enhancing the appeal of their visual content.
• Advertising and Multimedia Production: Advertisers and multimedia producers can generate custom music and soundtracks for commercials, videos, films, and games.