qwen3-omni-30b-a3b-captioner
Model Overview
This model is an open-source model built on Qwen3-Omni that automatically generates rich, detailed descriptions of complex audio — including speech, music, ambient sounds, and effects — without prompts. It detects emotions, musical styles, instruments, and sensitive information, making it ideal for audio analysis, security auditing, intent recognition, and editing.
How to Make a Call
API Schema
Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.
The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.
If set to True, the model response data will be streamed to the client as it is generated using server-sent events.
false
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async function main() {
const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
method: 'POST',
headers: {
'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'alibaba/qwen3-omni-30b-a3b-captioner',
messages:[
{
role:'user',
content: 'Hello'
}
]
}),
});
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
}
main();
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Code Example
import requests
import json # for getting a structured output with indentation
response = requests.post(
"https://api.aimlapi.com/v1/chat/completions",
headers={
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
"Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
"Content-Type":"application/json"
},
json={
"model": "alibaba/qwen3-omni-30b-a3b-captioner",
"messages": [
{
"role": "user",
"content": [
{
"type": "input_audio",
"input_audio": {
"data": "https://cdn.aimlapi.com/eagle/files/elephant/cJUTeeCmpoqIV1Q3WWDAL_vibevoice-output-7b98283fd3974f48ba90e91d2ee1f971.mp3"
}
}
]
}
]
}
)
data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))
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