ByteDance has launched a new music creation tool, Seed-Music, which is like a music magician that can generate high-quality music based on text descriptions, audio references, musical scores and even voice prompts. Seed-Music combines autoregressive language models and diffusion models to give users unprecedented control over music creation. Whether it is lyrics and music, melody adaptation, or voice-to-singing, Seed-Music can easily handle it. The editor of Downcodes will take you to learn more about this amazing music generation model.
Recently, ByteDance released a new music creation tool called Seed-Music. This magical music generation model allows you to easily generate music through a variety of input methods (such as text descriptions, audio references, musical scores, and even voice prompts). It is like having a music magician!
Seed-Music combines autoregressive language models and diffusion models to not only generate high-quality music works, but also allows you to precisely control the details of the music. Whether you want lyrics to accompany the music, or you want to adapt the melody, there's no problem here. You can even upload a short voice clip, and the system will automatically convert it into a complete song, which is convenient and efficient.
The powerful Seed-Music not only supports the generation of vocal and instrumental music, but also includes a series of functions such as singing voice synthesis, singing voice conversion and music editing, which can meet the needs of different users. You can generate pops through simple text descriptions, and you can also adjust the music style through audio prompts, which is really refreshing.
What’s more interesting is that Seed-Music’s architecture is divided into three modules: representation learning module, generation module and rendering module, which work together like a band to generate high-quality music from multi-modal inputs.
The representation learning module compresses the original audio signal into three intermediate representations, suitable for different music generation and editing tasks. The generation module converts user input into music representation through autoregressive models and diffusion models. The final rendering module is responsible for turning these intermediate representations into high-quality audio that your ears can enjoy.
In order to ensure the quality of music, Seed-Music uses a variety of technologies: the autoregressive language model gradually generates audio symbols, the diffusion model makes the music clearer through denoising, and the vocoder translates these music "codes" into readable High fidelity sound played.
The training process of Seed-Music is also very interesting, divided into three stages: pre-training, fine-tuning and post-training. Through large-scale music data, the model acquires basic capabilities, then improves the performance of specific tasks through fine-tuning, and finally continuously optimizes the generated results through reinforcement learning.
Project address: https://team.doubao.com/en/special/seed-music
The emergence of Seed-Music has undoubtedly brought new possibilities to music creation. Its convenient operation and powerful functions will greatly lower the threshold of music creation, allowing more people to experience the joy of music creation. We look forward to Seed-Music bringing more surprises in the future!