Create & Edit Music Easily with ByteDance’s Seed-Music AI Model
Discover how ByteDance’s Seed-Music AI Model revolutionizes music creation and editing with multimodal input, high-quality output, and real-time generation.
Overview
Seed-Music is a cutting-edge music generation model developed by ByteDance. It allows users to create and edit music effortlessly by inputting multimodal data such as text descriptions, audio references, music scores, and sound prompts. The model also offers convenient post-editing functions, enabling modifications to lyrics or melody after the initial creation.
Key Features of Seed-Music
High-Quality Music Generation
Seed-Music excels in generating both vocal and instrumental works. Users can input data through various methods, including text and audio, to achieve diverse music creation. The system combines an autoregressive language model with a diffusion model, ensuring precise control over the generated music while maintaining high quality.
Controlled Music Generation
This feature provides fine-grained control, allowing users to generate music that meets their specific requirements. Inputs can include lyrics, style descriptions, reference audio, and sheet music, offering a versatile approach to music creation.
Multimodal Input
Seed-Music supports multiple input methods, such as lyrics, music style descriptions, reference audio, sheet music, and voice prompts. This enables fine-grained control over the generated music, allowing users to specify the style, rhythm, and melody through text or audio references.
Vocal Synthesis and Conversion
- Singing Voice Synthesis: Generates natural and expressive singing voices in multiple languages.
- Zero-Sample Singing Conversion: Converts a 10-second voice or singing recording into music of different styles.
- Lyrics2Song: Converts input lyrics into vocal music with accompaniment, supporting both short and long music generation.
- Audio Cues and Style Transfer: Supports audio continuation and style transfer, generating new music of a similar style based on existing audio.
Instrumental Music Generation
Seed-Music can generate high-quality pure instrumental music, suitable for scenarios where lyrics are not required.
Music Post-Editing
The system supports modifications to lyrics and melody, allowing users to edit and adjust directly on the generated audio. This includes interactive tools for editing lyrics and melody, as well as music mixing and arrangement features.
Multi-Style and Multi-Language Support
Seed-Music can generate works covering various music styles, such as pop, classical, jazz, and electronic. It also supports multi-language singing generation, making it suitable for a global audience.
Real-Time Generation and Streaming Support
The model supports real-time music generation and streaming output, enhancing user interactivity and creative efficiency.
Architecture of Seed-Music
The architecture of Seed-Music consists of three main modules: the representation learning module, the generation module, and the rendering module. These modules work in tandem to generate high-quality music from multimodal inputs like text, audio, and sheet music.
Representation Learning Module
This module compresses the raw audio signal into three intermediate representations: audio symbols, symbolic music tags, and vocoder latent representations. Each representation is suitable for different music generation and editing tasks.
Generation Module
The generation module uses an autoregressive language model and a diffusion model to create corresponding music representations based on the user’s multimodal input.
Rendering Module
This module converts the generated intermediate representation into high-quality audio waveforms. It utilizes a diffusion model and a vocoder to render the final audio output.
Technical Methods of Seed-Music
Auto-Regressive Language Model
Based on user input such as lyrics, style descriptions, and audio references, this model generates audio symbols step by step. It is particularly effective for tasks that require strong context dependence, like lyrics generation and style control.
Diffusion Model
Ideal for complex music generation and editing tasks, the diffusion model generates clear music representations through gradual denoising. It is well-suited for tasks requiring multi-step predictions and high fidelity, such as fine audio editing.
Vocoder
The vocoder translates “music code” into high-quality sound files, generating music that can be played directly. Using variational autoencoder (VAE) technology, the vocoder can produce 44.1kHz high-fidelity stereo audio.
Intermediate Representation
Seed-Music employs three different intermediate representations for various generation tasks:
- Audio Tokens: Encode music features like melody, rhythm, and harmony, suitable for autoregressive models.
- Symbolic Music Tokens: Represent the melody and chords of music, ideal for sheet music generation and editing.
- Vocoder Latents: Handle complex sound details, suitable for fine-grained editing and generating intricate musical works.
Training and Reasoning of Seed-Music
Seed-Music’s model training is divided into three stages: pre-training, fine-tuning, and post-training.
Pre-Training
Establishes basic capabilities for generating music by pre-training models with large-scale music data.
Fine-Tuning
Fine-tunes the model through specific tasks or data to improve performance in particular generation tasks, enhancing musicality and generation accuracy.
Post-Training (Reinforcement Learning)
Optimizes the controllability and music quality of the generated results through reinforcement learning. Reward models such as the matching degree between lyrics and audio and the consistency of music structure are used to optimize output quality.
During inference, Seed-Music employs streaming generation technology, enabling users to experience the generation process in real-time and provide feedback based on the generated content.
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