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How to fine-tune an ACE-Step LoRA on your own tracks with Remiqora

Upload audio, auto-label it, review the labels, train a LoRA adapter and use it on the generation form.

  • Content
  • Advanced
  • 3 hours
  • From the README
  • Any agent

Authorization notice. Remiqora is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Voice cloning. Training on a person's voice can produce a voice clone, so use only recordings you have the right to use and do not present the output as someone else's voice.

Who it is forA producer who wants ACE-Step to generate in a consistent style or with a specific voice from their own recordings.

The problemFine-tuning a music model usually needs a console, preprocessing scripts and manual labeling.

The outcomeA LoRA adapter added to the LoRA list on the ACE-Step form, with live training progress and a TensorBoard link.

What you need first

  • A computer with a consumer GPU; the README describes the studio as GPU-powered and local
  • The Remiqora desktop installer from the project's GitHub page, or the install steps in its README
  • Enough free disk space for the models you choose; the installer lets you skip YuE2, stem separation or arranger base models and add them later
  • For prompt writing, the Qwen3.5 assistant downloaded from the Models page

Steps

  1. Open the LoRA training tab on the ACE-Step page and upload your audio files by drag and drop, or point to a server folder.
  2. Enter a trigger word and set the all tracks are instrumental flag if it applies.
  3. Run automatic labeling, then review and fix descriptions, genres and tags in the table.
  4. Run preprocessing, then start training with your rank, alpha, learning rate, epochs and batch size.
  5. Select the finished adapter in the LoRA list on the ACE-Step generation form and adjust its strength.

Prompt for your agent

Using the repo inikolax/remiqora, create a LoRA dataset from the folder <dataset folder> with the trigger word <trigger word> and train an ACE-Step adapter. Expected output: a finished adapter listed in the ACE-Step LoRA list. Verify that the adapter appears in the list and that a short test generation with it sounds different from the base model.

Caveat. The README describes the training pipeline but not its hardware needs, so check that your GPU has enough memory before starting a long run.

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