ace-step/ACE-Step

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4788 stars · 616 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

ACE-Step is an open-source foundation model for music generation that combines diffusion-based generation with a deep compression autoencoder and linear transformer to synthesize up to 4 minutes of music in ~20 seconds on an A100 GPU. It supports text-to-music and lyrics-to-song generation plus controls like voice cloning, lyric editing, remixing, and track generation.

Use cases

• generate a full song from lyrics and a style prompt
• create background music for videos from a text description
• clone a voice and generate singing vocals
• remix or edit lyrics of an existing generated track
• generate instrumental accompaniment for a vocal track
• fine-tune a music generation model for custom sub-tasks

When to choose

• you need fast, long-form (up to 4 min) music or song generation with strong lyric alignment
• you want an open-source, trainable foundation model for building music AI tools
• you need fine-grained controls like voice cloning, remixing, or stem generation

When to avoid

• you only need short sound effects or speech synthesis rather than structured music
• you lack a GPU and cannot tolerate heavy inference requirements
• you need a polished commercial music production tool rather than a model and codebase

Facets

library · maturity active

audio-processing machine-learning deep-learning llm-inference artificial-intelligence deep-learning media python cross-platform music-generation text-to-music diffusion-model song-generation voice-cloning lyrics-to-song foundation-model audio-synthesis audio gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ace-step/ACE-Step")