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What is Seed-Music?

Seed-Music is a comprehensive platform designed for the creation and modification of high-quality musical compositions, enabling users to produce both vocal and instrumental works from a variety of multimodal inputs, including lyrics, stylistic descriptions, sheet music, audio samples, or even vocal suggestions. This cutting-edge framework also supports the post-production editing of pre-existing tracks, allowing users to make direct modifications to melodies, instrumentations, timbres, or lyrics. It utilizes a combination of autoregressive language modeling and diffusion processes, structured into a three-phase pipeline: the first phase is representation learning, which encodes raw audio into intermediate formats such as audio tokens and symbolic music tokens; the second phase is generation, which converts these varied inputs into musical representations; and the final phase is rendering, which changes these representations into high-fidelity sound outputs. Additionally, Seed-Music's features encompass the transformation of lead sheets into complete songs, synthesis of singing voices, voice modulation, audio continuation, and style adaptation, offering users detailed control over the musical elements and composition. This extensive versatility positions it as an essential tool for musicians and music producers eager to delve into new realms of creativity and innovation. Ultimately, Seed-Music not only enhances the creative process but also broadens the possibilities for musical expression in the digital age.

What is ModelScope?

This advanced system employs a complex multi-stage diffusion model to translate English text descriptions into corresponding video outputs. It consists of three interlinked sub-networks: the first extracts features from the text, the second translates these features into a latent space for video, and the third transforms this latent representation into a final visual video format. With around 1.7 billion parameters, the model leverages the Unet3D architecture to facilitate effective video generation through a process of iterative denoising that starts with pure Gaussian noise. This cutting-edge methodology enables the production of engaging video sequences that faithfully embody the stories outlined in the input descriptions, showcasing the model's ability to capture intricate details and maintain narrative coherence throughout the video. Furthermore, this system opens new avenues for creative expression and storytelling in digital media.

Media

Media

Integrations Supported

Integrations Supported

CodeQwen
Qwen
Qwen 4
Qwen-7B
Qwen-Image
Qwen2
Qwen2-VL
Qwen2.5
Qwen2.5-1M
Qwen2.5-Max
Qwen2.5-VL
Qwen3
Qwen3.7-Max
Qwen3.7-Plus
Qwen3.8-27B
Qwen3.8-Flash-Next
Qwen3.8-Max
Step 3.5 Flash
Step 5 Preview
Yi-Large

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

ByteDance

Date Founded

2012

Company Location

China

Company Website

seed.bytedance.com/en/seed-music

Company Facts

Organization Name

Alibaba Cloud

Company Location

China

Company Website

modelscope.cn/

Categories and Features

AI Music Generators

Not specified

Categories and Features

AI Gateways

Not specified

AI Inference

Not specified

AI Tools

Not specified

AI Video Generators

Not specified

ML Model Deployment

Not specified

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