# ace-step/ACE-Step

ACE-Step: A Step Towards Music Generation Foundation Model

Repository: https://github.com/ace-step/ACE-Step
Canonical: https://ross.abutalabs.com/products/ace-step
Homepage: https://ace-step.github.io/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-02-15T04:57:54+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 67, release rhythm 35, longevity 35
- inputs: {"age_days": 492, "days_push": 199, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4788, forks 616 (observed 2026-08-28T04:08:59.850167+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: audio-processing, machine-learning, deep-learning, llm-inference
- domain: artificial-intelligence, deep-learning, media
- platform: python, cross-platform
- tags: music-generation, text-to-music, diffusion-model, song-generation, voice-cloning, lyrics-to-song, foundation-model, audio-synthesis, audio, gpu, linux

## Member repositories
- ace-step/ACE-Step (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:59.850167+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:41.562099+00:00, confidence not recorded.
  - readme: https://github.com/ace-step/ACE-Step (fetched 2026-08-28T04:08:59.850167+00:00, sha 224864590efa)
  - homepage: https://ace-step.github.io/ (fetched 2026-08-29T09:01:50.103811+00:00, sha fdc892468fd3)
- Data as of 2026-08-30T08:39:29.467469+00:00.