aura
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.
API Schema
The text content to be converted to speech.
The file format wrapper for the output audio. The available options depend on the encoding type.
Specifies the expected encoding of your audio output
linear16
Possible values: The sample rate for the output audio. Based on the encoding, different sample rates are supported. For some encodings, the sample rate is not configurable
POST /v1/tts HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 105
{
"model": "#g1_aura-angus-en",
"text": "text",
"container": "text",
"encoding": "linear16",
"sample_rate": "text"
}
{
"metadata": {
"transaction_key": "text",
"request_id": "text",
"sha256": "text",
"created": "2025-07-14T07:25:37.671Z",
"duration": 1,
"channels": 1,
"models": [
"text"
],
"model_info": {
"ANY_ADDITIONAL_PROPERTY": {
"name": "text",
"version": "text",
"arch": "text"
}
}
}
}
Code Example
import os
import requests
def main():
url = "https://api.aimlapi.com/v1/tts"
headers = {
# Insert your AI/ML API key instead of <YOUR_AIMLAPI_KEY>:
"Authorization": "Bearer <YOUR_AIMLAPI_KEY>",
}
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()
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