POST
Train a LoRA adapter on a corpus of reference tracks. Captures genre conventions, instrumentation choices, or production characteristics that the base music model doesn’t expose as a prompt-able style.

Authorization

string
required
Bearer token. Bearer API_key.

Request Body

string
required
Reference to the training dataset. Accepts either:
  • A URL to a zip archive of reference tracks (mp3 / wav)
  • A file id returned from file upload
string
required
Stable snake-case identifier for the trained adapter. Passed as lora_name on music generation calls.

Tips

  • Dataset size: 10–25 tracks for genre adapters; smaller curated sets often outperform large noisy ones.
  • Length: 30–120 second clips are the sweet spot. Full songs are automatically segmented.
  • Vocals vs instrumental: if the goal is the instrumentation treatment, pre-process the dataset with voice isolate and use the return_instrumental flag to keep only the no-vocals stems.
  • Status: poll Training Status.