# Stable Audio 3 Trainer API

> Addestra una LoRA su Stable Audio 3 a partire da un archivio zip di clip audio con didascalie testuali corrispondenti

Esegue il fine-tuning di un checkpoint di base (medium-base, small-music-base o small-sfx-base) su un archivio zip `audio_data_url`, in cui ogni file audio ha una didascalia `.txt` con lo stesso nome. Puoi regolare `number_of_steps` (fino a 20000), `learning_rate`, `rank` (da 1 a 256), `adapter_type` e `batch_size`, oppure riprendere da un checkpoint `.safetensors`. Restituisce i pesi LoRA e un file di configurazione.

- Model id: `stable-audio-3-trainer`
- Made by: Stability AI
- Category: Training
- Output: file
- Price per run: from $0.003 / run (cheapest run $0.003 (3 API credits))
- Run: `POST https://api.doitong.com/v1/run/stable-audio-3-trainer`
- Quote: `POST https://api.doitong.com/v1/quote/stable-audio-3-trainer`
- Docs page: https://doitong.com/it/developers/models/stable-audio-3-trainer

## Pricing

Price: **from $0.003 / run** (the lowest tier; the quote prices your exact input). Price per run: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $3.00 = 3000 API credits (1 run × $3.00).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.com/v1/quote/stable-audio-3-trainer` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `audio_data_url` |  | string | yes |  |  | URL dello zip del dataset, con ogni coppia audio/didascalia memorizzata insieme usando nomi base corrispondenti: ad esempio, `clip.wav` corrisponde a `clip.txt`. Le didascalie devono usare l'estensione `.txt`. |
| `model` |  | enum (string) | no | `medium-base` | `medium-base`, `small-music-base`, `small-sfx-base` | Checkpoint di base per il fine-tuning di Stable Audio 3. |
| `number_of_steps` |  | integer | no | `1000` | 1 – 20,000 | Numero di passaggi per l'addestramento LoRA. |
| `learning_rate` |  | number | no | `0.0001` | ≤ 0.01, > 0 | Tasso di apprendimento applicato da AdamW ai parametri LoRA. |
| `rank` |  | integer | no | `16` | 1 – 256 | Rank dell'adattatore LoRA. |
| `adapter_type` |  | enum (string) | no | `dora-rows` | `lora`, `dora`, `dora-rows`, `dora-cols`, `bora`, `lora-xs`, `dora-rows-xs`, `dora-cols-xs`, `bora-xs` | Famiglia di adattatori usata per l'addestramento LoRA. |
| `duration` |  | number \| null | no |  | 1 – 380 | Durata del ritaglio e del padding in secondi, limitata in ogni caso dalla lunghezza nativa di addestramento del modello selezionato. Fornisci una durata oppure omettila per usare il clip più lungo rilevato nel dataset, entro tale limite. |
| `batch_size` |  | integer | no | `1` | 1 – 8 | Dimensione del batch usata durante l'addestramento. |
| `seed` |  | integer | no | `42` | 0 – 2,147,483,647 | Seed per la randomizzazione. |
| `base_precision` |  | enum (string) | no | `bf16` | `bf16`, `bfloat16`, `fp16`, `float16` | Precisione usata dai pesi di base congelati; i parametri LoRA restano fp32. |
| `include` |  | array of string \| null | no |  |  | Sottostringhe dei nomi dei moduli che limitano i punti in cui viene aggiunto LoRA: lo ricevono solo i moduli corrispondenti. |
| `exclude` |  | array of string \| null | no |  |  | Sottostringhe dei nomi usate per escludere i moduli corrispondenti. |
| `lora_checkpoint_url` |  | string \| null | no |  |  | URL facoltativo di un checkpoint LoRA `.safetensors` da cui riprendere l'addestramento. |
| `pre_encode` |  | boolean | no | `false` |  | Pre-codifica dell'archivio audio in latenti SAME prima dell'addestramento LoRA. |

## Example input

Required parameters only:

```json
{
  "audio_data_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}
```

A fuller example:

```json
{
  "audio_data_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/stable-audio-3-trainer?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "audio_data_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/stable-audio-3-trainer?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "audio_data_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "audio_data_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/stable-audio-3-trainer", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.com/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "stable-audio-3-trainer",
      "input": {
        "audio_data_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "stable-audio-3-trainer",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 3000,
    "usd": "3.00",
    "charged": 3000,
    "refunded": 0
  },
  "input": {
    "audio_data_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
  },
  "output": {
    "lora_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "config_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    }
  },
  "outputs": [
    {
      "type": "file",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `lora_file` | object {url, content_type, file_name, file_size} | Pesi LoRA addestrati per Stable Audio 3. |
| `config_file` | object {url, content_type, file_name, file_size} | Metadati JSON che descrivono l'esecuzione dell'addestramento e il modello compatibile per l'inferenza. |

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## Links

- HTML page: https://doitong.com/developers/models/stable-audio-3-trainer
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=stable-audio-3-trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/stable-audio-3-trainer