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fal-ai/stable-audio-3-trainer

Stable Audio 3 LoRA Trainer fine-tunes Stable Audio 3 base models on paired audio-caption datasets, producing compact LoRA weights that adapt generation toward a custom music style, sound palette, or domain.
Training
Commercial use
Schema
LLMs
PlaygroundAPI

Input

Additional Settings

Customize your input with more control.

Your request will cost $3.00 per 1000-step training run. It scales per step, so a 2000-step training run will cost $6.00. Training with batch_size > 1 bills additional units proportional to batch_size × clip duration.

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Training history

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Note: these are the most recent training requests. For the full history, check the requests tab.

Stable Audio 3 LoRA Trainer

Fine-tune a Stable Audio 3 base model into a LoRA that adapts it to your own style, instrument, or sound. The output is a small .safetensors file you load on the Stable Audio 3 inference endpoints (the /lora variants).

Dataset

Provide a .zip archive (via audio_data_url) of audio clips, each paired with a .txt caption that has the same basename:

my_dataset.zip
├── clip1.wav
├── clip1.txt        # text prompt describing clip1
├── clip2.flac
├── clip2.txt
└── ...
  • Audio: WAV, FLAC, MP3, OGG, OPUS, or AIFF (WAV/FLAC recommended). 4–2000 clips.
  • Captions: one non-empty UTF-8 .txt per clip. Describe the audio the way you would prompt the model — e.g. genre, instruments, mood, and BPM for music; source, action, and recording character for SFX.
  • Mixed clip lengths are fine; clips longer than the model's max are cropped.

Models (model)

ModelUse forMax clip length
medium-base (default)music, stems, SFX~6 min (380 s)
small-music-basemusic, stems2 min
small-sfx-basesound effects2 min

Key parameters

ParameterDefaultNotes
number_of_steps10001–20000. ~500–2000 is typical; more = stronger adaptation.
rank161–256. Higher = more expressive and a larger file. -xs adapters require ≤ 17.
learning_rate1e-4AdamW learning rate for the LoRA.
adapter_typedora-rowsOne of lora, dora, dora-rows, dora-cols, bora, or their -xs variants (minimal parameters/VRAM).
durationautoCrop length in seconds. Auto-detected from the longest clip and capped at the model's max.
batch_size11–8, must be ≤ the number of clips. Values > 1 bill extra units proportional to batch size × clip duration.
base_precisionbf16Precision of the frozen base weights (LoRA stays fp32).
seed42For reproducible runs.

Advanced: include / exclude (limit which layers receive LoRA), lora_checkpoint_url (resume from an existing .safetensors), and pre_encode (pre-encode audio to latents before training).

Output

  • lora_file — the trained .safetensors LoRA.
  • config_file — JSON metadata for the run (model, steps, rank, etc.).

Load lora_file on the matching Stable Audio 3 inference endpoint's /lora variant (e.g. /medium/text-to-audio/lora) to generate with your fine-tune.