# Stable Audio 2.5 Audio-to-Audio

> Transform an existing audio clip into new music or sound effects guided by a text prompt — restyle, re-instrument, or reimagine a source track, returned as a WAV.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/stability-ai/stable-audio-2.5/audio-to-audio`
- **Model ID**: `stability-ai/stable-audio-2.5/audio-to-audio`
- **Category**: audio-to-audio
- **Kind**: inference
- **Tags**: stability-ai, stable-audio, stable-audio-2.5, audio-to-audio, audio-transformation, music, audio

## Pricing

- **Price**: $0.2 per output

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/stability-ai/stable-audio-2.5/audio-to-audio` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/stability-ai/stable-audio-2.5/audio-to-audio/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/stability-ai/stable-audio-2.5/audio-to-audio/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/stability-ai/stable-audio-2.5/audio-to-audio/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`seed`** (`integer`, _optional_):
  Random seed for reproducible generation. Leave empty for a random result.

- **`prompt`** (`string`, _required_):
  The text prompt guiding the transformation. Describe the genre, instrumentation, mood, and tempo you want the source audio reshaped into.

- **`strength`** (`number`, _optional_):
  How much the source audio is transformed: near 0 keeps it almost identical to the input, near 1 ignores the input and follows only the prompt.
  - Default: `0.8`
  - Range: `0.01` to `1`

- **`audio_url`** (`string`, _required_):
  The source audio clip to transform (accepted formats: mp3, ogg, wav, m4a, aac).

- **`total_seconds`** (`integer`, _optional_):
  Duration of the generated audio in seconds (1-190). Defaults to the source clip's length if unset. Billing is a flat rate per generation regardless of length.
  - Range: `1` to `190`

- **`guidance_scale`** (`number`, _optional_):
  Classifier-free guidance scale; higher values follow the prompt more strictly.
  - Default: `1`
  - Range: `1` to `25`

- **`num_inference_steps`** (`integer`, _optional_):
  Number of denoising steps. More steps can improve quality at the cost of speed.
  - Default: `8`
  - Range: `4` to `8`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "transform into a lush cinematic orchestral arrangement with sweeping strings, warm brass, and soft timpani",
  "strength": 0.8,
  "audio_url": "https://media.modelrunner.ai/XXwzWR14Uzg7pOUOaeZqI.wav",
  "total_seconds": 30,
  "guidance_scale": 1,
  "num_inference_steps": 8
}
```

**Output**

```json
"https://media.modelrunner.ai/1y8oVNX6FKejKLGrlHSNt.wav"
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/stability-ai/stable-audio-2.5/audio-to-audio \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "transform into a lush cinematic orchestral arrangement with sweeping strings, warm brass, and soft timpani",
  "strength": 0.8,
  "audio_url": "https://media.modelrunner.ai/XXwzWR14Uzg7pOUOaeZqI.wav",
  "total_seconds": 30,
  "guidance_scale": 1,
  "num_inference_steps": 8
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

```javascript
import { modelrunner } from "@modelrunner/client";

const result = await modelrunner.subscribe("stability-ai/stable-audio-2.5/audio-to-audio", {
  input: {
  "prompt": "transform into a lush cinematic orchestral arrangement with sweeping strings, warm brass, and soft timpani",
  "strength": 0.8,
  "audio_url": "https://media.modelrunner.ai/XXwzWR14Uzg7pOUOaeZqI.wav",
  "total_seconds": 30,
  "guidance_scale": 1,
  "num_inference_steps": 8
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "stability-ai/stable-audio-2.5/audio-to-audio",
        arguments={
  "prompt": "transform into a lush cinematic orchestral arrangement with sweeping strings, warm brass, and soft timpani",
  "strength": 0.8,
  "audio_url": "https://media.modelrunner.ai/XXwzWR14Uzg7pOUOaeZqI.wav",
  "total_seconds": 30,
  "guidance_scale": 1,
  "num_inference_steps": 8
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/stability-ai/stable-audio-2.5/audio-to-audio)
- [OpenAPI Schema](https://modelrunner.ai/models/stability-ai/stable-audio-2.5/audio-to-audio/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/stability-ai/stable-audio-2.5/audio-to-audio/llms.txt)