# aura
This documentation is valid for the following list of our models
## Model Overview
Deepgram Aura is the first text-to-speech (TTS) AI model designed for real-time, conversational AI agents and applications. It delivers human-like voice quality with unparalleled speed and efficiency. It has dozen natural, human-like voices with lower latency than any comparable voice AI alternative and supports seamless integration with Deepgram's industry-leading Nova speech-to-text API.
## Setup your API Key
If you don’t have an API key for the AI/ML API yet, feel free to use our [Quickstart guide](https://docs.aimlapi.com/quickstart/setting-up).
## API Schema
## POST /v1/tts
>
```json
{"openapi":"3.0.0","info":{"title":"AIML API","version":"1.0.0"},"servers":[{"url":"https://api.aimlapi.com"}],"paths":{"/v1/tts":{"post":{"operationId":"_v1_tts","requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"model":{"type":"string","enum":["aura-asteria-en","#g1_aura-angus-en","#g1_aura-arcas-en","#g1_aura-asteria-en","#g1_aura-athena-en","#g1_aura-helios-en","#g1_aura-hera-en","#g1_aura-luna-en","#g1_aura-orion-en","#g1_aura-orpheus-en","#g1_aura-perseus-en","#g1_aura-stella-en","#g1_aura-zeus-en"]},"text":{"type":"string","description":"The text content to be converted to speech."},"container":{"type":"string","description":"The file format wrapper for the output audio. The available options depend on the encoding type."},"encoding":{"type":"string","enum":["linear16","mulaw","alaw","mp3","opus","flac","aac"],"default":"linear16","description":"Specifies the expected encoding of your audio output"},"sample_rate":{"type":"string","description":"Audio sample rate in Hz."},"stream":{"type":"boolean","default":true}},"required":["model","text"],"title":"aura-asteria-en"}}}},"responses":{"200":{"content":{"application/json":{"schema":{"type":"object","properties":{"audio":{"type":"string","format":"uri"},"meta":{"type":"object","nullable":true,"properties":{"usage":{"type":"object","nullable":true,"properties":{"credits_used":{"type":"number","description":"The number of tokens consumed during generation."},"usd_spent":{"type":"number","description":"The total amount of money spent by the user in USD."}},"required":["credits_used","usd_spent"]}},"description":"Additional details about the generation."}},"required":["audio"]}},"audio/wav":{"schema":{"type":"string","format":"binary","description":"Audio stream"}}}}}}}}}
```
## Code Example
{% tabs %}
{% tab title="Python" %}
{% code overflow="wrap" %}
```python
import os
import requests
def main():
url = "https://api.aimlapi.com/v1/tts"
headers = {
# Insert your AI/ML API key instead of :
"Authorization": "Bearer ",
}
payload = {
"model": "#g1_aura-athena-en",
"text": '''
Cities of the future promise to radically transform how people live, work, and move.
Instead of sprawling layouts, we’ll see vertical structures that integrate residential, work, and public spaces into single, self-sustaining ecosystems.
Architecture will adapt to climate conditions, and buildings will be energy-efficient—generating power through solar panels, wind turbines, and even foot traffic.
'''
}
response = requests.post(url, headers=headers, json=payload, stream=True)
# result = os.path.join(os.path.dirname(__file__), "audio.wav") # if you run this code as a .py file
result = "audio.wav" # if you run this code in Jupyter Notebook
with open(result, "wb") as write_stream:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
write_stream.write(chunk)
print("Audio saved to:", result)
main()
```
{% endcode %}
{% endtab %}
{% tab title="JavaScript" %}
{% code overflow="wrap" %}
```javascript
const https = require("https");
const fs = require("fs");
// Insert your AI/ML API key instead of :
const apiKey = "";
const data = JSON.stringify({
model: "#g1_aura-athena-en",
text: `
Cities of the future promise to radically transform how people live, work, and move.
Instead of sprawling layouts, we’ll see vertical structures that integrate residential, work, and public spaces into single, self-sustaining ecosystems.
Architecture will adapt to climate conditions, and buildings will be energy-efficient—generating power through solar panels, wind turbines, and even foot traffic.
`
});
const options = {
hostname: "api.aimlapi.com",
path: "/v1/tts",
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
"Content-Length": Buffer.byteLength(data),
}
};
const req = https.request(options, (res) => {
if (res.statusCode >= 400) {
let error = "";
res.on("data", chunk => error += chunk);
res.on("end", () => {
console.error(`Error ${res.statusCode}:`, error);
});
return;
}
const file = fs.createWriteStream("audio.wav");
res.pipe(file);
file.on("finish", () => {
file.close();
console.log("Audio saved to audio.wav");
});
});
req.on("error", (e) => {
console.error("Request error:", e);
});
req.write(data);
req.end();
```
{% endcode %}
{% endtab %}
{% endtabs %}
Response
```
Audio saved to: audio.wav
```
{% embed url="" %}
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.aimlapi.com/api-references/speech-models/text-to-speech/deepgram/aura.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.