> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flex.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Generate Audio with Stable Audio Open on FlexAI Endpoints
> Deploy Stable Audio Open 1.0 for audio generation via FlexAI inference endpoints. High-quality output, hardware-agnostic, scales automatically.
This experiment demonstrates how to deploy and use a text-to-audio model (Stable Audio Open 1.0) using Flexai's inference serving capabilities.
## Prerequisites
Before starting, make sure you have:
* A Flexai account with access to the platform
* A Hugging Face token with access to the `stabilityai/stable-audio-open-1.0` model
* The `flexai` CLI installed and configured
## Setup FlexAI Secret for Hugging Face Token
First, create a FlexAI secret that contains your Hugging Face token to access the inference model:
```bash theme={null}
# Enter your HF token value when prompted
flexai secret create MY_HF_TOKEN
```
Make sure your Hugging Face token has access to the `stabilityai/stable-audio-open-1.0` model. You may need to accept the model's license terms on Hugging Face first.
## Start the FlexAI Inference Endpoint
Start the FlexAI endpoint for the Stable Audio Open 1.0 model:
```bash theme={null}
INFERENCE_NAME=stable-audio-open
flexai inference serve $INFERENCE_NAME --runtime flexserve --hf-token-secret MY_HF_TOKEN -- --task text-to-audio --model stabilityai/stable-audio-open-1.0
```
This command will:
* Create an inference endpoint named `stable-audio-open`
* Use the `flexserve` runtime optimized for text-to-audio tasks
* Load the Stable Audio Open 1.0 model from Hugging Face
* Configure it for text-to-audio generation
## Get Endpoint Information
Once the endpoint is deployed, you'll see the API key displayed in the output. Store it in an environment variable:
```bash theme={null}
export INFERENCE_API_KEY=
```
Then retrieve the endpoint URL:
```bash theme={null}
export INFERENCE_URL=$(flexai inference inspect $INFERENCE_NAME -j | jq .config.endpointUrl -r)
```
You'll notice these `export` lines use the `jq` tool to extract values from the JSON output of the `inspect` command.
If you don't have it already, you can get `jq` from its official website: [https://jqlang.org/](https://jqlang.org/)
## Generate Audio
Now you can generate audio by making HTTP POST requests to your endpoint. Here are some examples:
### Example 1: Relaxing Music
```bash theme={null}
curl -X POST \
-H "Authorization: Bearer $INFERENCE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"inputs": "Relaxing piano music with soft ambient sounds, calm and peaceful",
"parameters": {
"audio_end_in_s": 10.0,
"num_inference_steps": 200,
"guidance_scale": 7.0,
"seed": 42
}
}' \
-o relaxing_music.wav \
"$INFERENCE_URL/v1/audios/generations"
```
### Example 2: Nature Soundscape
```bash theme={null}
curl -X POST \
-H "Authorization: Bearer $INFERENCE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"inputs": "Forest ambience with birds chirping and a gentle stream flowing",
"parameters": {
"audio_end_in_s": 15.0,
"num_inference_steps": 200,
"guidance_scale": 7.0,
"negative_prompt": "distorted, low quality, muffled",
"seed": 123
}
}' \
-o nature_sounds.wav \
"$INFERENCE_URL/v1/audios/generations"
```
### Example 3: Electronic Beat
```bash theme={null}
curl -X POST \
-H "Authorization: Bearer $INFERENCE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"inputs": "Upbeat electronic music with synthesizers and energetic drums, 128 bpm",
"parameters": {
"audio_end_in_s": 20.0,
"num_inference_steps": 200,
"guidance_scale": 7.0,
"seed": 456
}
}' \
-o electronic_beat.wav \
"$INFERENCE_URL/v1/audios/generations"
```
These will save the generated audio files in your current directory.
## Parameters Explanation
The API accepts the following parameters:
* **inputs**: The text prompt describing the audio you want to generate
* **audio\_length\_in\_s**: Output audio duration in seconds (typically up to 47 seconds for Stable Audio Open)
* **num\_inference\_steps**: Number of denoising steps (higher = better quality but slower, recommended: 100-200)
* **guidance\_scale**: As you increase the value, the model tries harder to match your prompt.
* **negative\_prompt**: Description of what you don't want in the audio (helps improve quality)
* **seed**: Random seed for reproducible results
⚡ Deploy this on FlexAI
Any model, NVIDIA or AMD, live in under 60 seconds. No code changes, no infra management, hardware-agnostic.