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Audio & SpeechOpen-Source AIQiita 機械学習Published: Oct 26, 2026, 06:00 JST

ACE-Step text-to-music model runs locally, per tutorial

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ACE-Step text-to-music model runs locally, per tutorial

3 Key Points

  1. What happened

    A Qiita article walks through installing and running ACE-Step, an open-source music generation model, on a local machine, noting it can create songs up to four minutes long from text and that on an A100 GPU a four-minute track takes about 20 seconds.

  2. Why it matters

    The guide shows that generating full-length songs from text no longer requires a large cloud setup, so musicians and developers can experiment with composition on their own hardware.

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Context & Analysis

ACE-Step combines a diffusion model with Sana's Deep Compression AutoEncoder (DCAE) and a lightweight linear Transformer. This architecture is what enables the fast generation described in the tutorial. The model also offers advanced controls such as variation generation, repainting, lyric editing, and voice cloning, which go beyond simple text-to-music creation. For readers with limited hardware, the guide provides specific launch options aimed at environments with about 8GB of VRAM. The model is available through GitHub, Hugging Face, and an official website, and a technical report is also linked. The tutorial positions ACE-Step as practical for BGM generation and composition trial and error, suggesting it may be useful for creators who want to iterate quickly on musical ideas.

FAQ
What hardware do I need to run ACE-Step locally?
The tutorial recommends launch options for environments with about 8GB of VRAM. On macOS, users should add --bf16 false.
How fast is ACE-Step at generating music?
On an A100 GPU, it can synthesize four minutes of music in about 20 seconds, which is 15 times faster than LLM-based models. Its real-time factor is 27.27x at 27 steps.
Does ACE-Step support Japanese lyrics?
Yes, it supports lyrics in 19 languages including English, Chinese, and Japanese. Japanese is among the top 10 languages supported.
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