OpenViSTAI · Agents · Blockchain · Crypto
Menu
AI Models

Yue

YuE2 is an Apache-2.0-licensed music-generation model that combines symbolic planning with audio generation. It creates songs from lyrics and style prompts, can expose editable melody-and-chord plans, and supports zero-shot covers and music editing.

Independent OpenViST project profile, based on linked sources.

Yue — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use YuE2 when a music-generation workflow benefits from symbolic control over melody and chords, or when adapting a transcribed song to new lyrics or a different style.

Who it is for

  • Musicians and composers who want to generate songs from lyrics, style prompts or symbolic scores.
  • Developers building music-generation or music-editing workflows around a locally run Python model.
  • People who want to try song generation through the linked browser demo without installing the model.

Documented uses

  • Generate a complete song with vocals and accompaniment from lyrics and a style prompt.
  • Inspect or revise a generated melody-and-chord plan before audio rendering.
  • Create a cover by transcribing a source recording, then applying new lyrics or a target style.
  • Supply an ABC score as input to guide generation.
Before you choose

Requirements & limits

The repository links a hosted browser demo described as free and requiring no installation. Local use is documented for Linux with Python 3.12 and an NVIDIA GPU with BF16 support and 24 GB VRAM.

  • The local workflow has explicit Linux, Python 3.12 and GPU requirements.
  • Model files download from Hugging Face on first use.
  • Generation can use full symbolic planning, melody-only planning, direct lyric-and-style generation, or a supplied ABC score.
  • The repository preserves the original YuE-v1 code, documentation and license on a separate branch.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up YuE2 locally on Linux

Install the documented local Python package and generate a first song.

Before you start

  • Linux
  • Python 3.12
  • NVIDIA GPU with BF16 support and 24 GB VRAM
  • Internet access for model files downloaded from Hugging Face on first use

Steps

  1. Clone the YuE repository and change into its directory.

  2. Create and activate a Python 3.12 virtual environment.

  3. Upgrade pip and install the package from the repository.

  4. Run the provided examples/generate.py command with an output directory.

Expected outcome

The documented output includes outputs/first-song/audio.flac; the directory also retains score and generation artifacts.

Guide limitations

  • The documented local quick start specifies Linux and the stated GPU requirements.
  • Model files are downloaded from Hugging Face on first use.
Source-based practical guide

Prepare a YuE2 song cover

Use a transcribed melody score, new lyrics or style, and YuE2's melody-planning mode to prepare a cover.

Before you start

  • A source recording
  • SheetSage2 for transcription
  • New lyrics or a target style
  • YuE2 local Python setup

Steps

  1. Transcribe the source recording with SheetSage2.

  2. Review the resulting melody ABC score.

  3. Provide the score and new lyrics or target style to YuE2.

  4. Use cot="melody" with a score that has no chord symbols.

Expected outcome

YuE2 generates a cover with accompaniment that can adapt to the target style.

Guide limitations

  • The cover workflow requires a transcribed and reviewed melody score.
  • The guidance specifies melody mode and a score without chord symbols.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
multimodal-art-projection/YuE · Official homepage title · retained in its source language

Related options

YuE-v1 — The repository points readers seeking the original YuE to a separate branch preserving its code, documentation and license.