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Summarization

Generate summaries of your audio transcripts using LLM Gateway

For the complete documentation index, see llms.txt

Supported models: Universal-3 Pro (universal-3-pro), Universal-2 (universal-2)

Supported regions: US and EU

Note: The summarization, summary_model, and summary_type parameters on the transcription API are deprecated. Use LLM Gateway for summaries.

Two-step approach:

1. Transcribe audio via `POST
2. Send transcript text to LLM Gateway via `POST

Key LLM Gateway parameters:

• model (string) - LLM model to use (e.g., claude-sonnet-4-6)
• messages (array) - Messages including your summarization prompt and transcript text
• max_tokens (number) - Maximum response tokens

cURL quickstart (Step 1 - Transcribe):

curl  \
  --header "Authorization: YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "audio_url": "YOUR_AUDIO_URL",
    "speech_models": ["universal-3-pro", "universal-2"]
  }'

Poll GET /v2/transcript/{id} until status is completed, then extract the transcript text.

cURL quickstart (Step 2 - Summarize via LLM Gateway):

curl  \
  --header "Authorization: YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "claude-sonnet-4-6",
    "messages": [
      {"role": "user", "content": "Provide a brief summary of the transcript.\n\nTranscript: YOUR_TRANSCRIPT_TEXT"}
    ],
    "max_tokens": 1000
  }'

The summary is in response.choices[0].message.content.

US & EU

Generate summaries of your audio transcripts using LLM Gateway. This approach gives you full control over the summary format, length, and style by customizing your prompt.

The summarization, summary_model, and summary_type parameters on the transcription API are deprecated. Use LLM Gateway as shown below for more flexible and powerful summaries.

Quickstart

import requests
import time

base_url = ""
headers = {"authorization": "<YOUR_API_KEY>"}

# Step 1: Transcribe your audio file
audio_url = ""

data = {
    "audio_url": audio_url,
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": True
}

response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
transcript_id = response.json()['id']
polling_endpoint = base_url + "/v2/transcript/" + transcript_id

while True:
    transcription_result = requests.get(polling_endpoint, headers=headers).json()
    if transcription_result['status'] == 'completed':
        break
    elif transcription_result['status'] == 'error':
        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
    else:
        time.sleep(3)

# Step 2: Generate a summary using LLM Gateway
prompt = "Provide a brief summary of the transcript in bullet point format."

llm_gateway_data = {
    "model": "claude-sonnet-4-6",
    "messages": [
        {"role": "user", "content": f"{prompt}\n\nTranscript: {transcription_result['text']}"}
    ],
    "max_tokens": 1000
}

response = requests.post(
    "",
    headers=headers,
    json=llm_gateway_data
)

result = response.json()["choices"][0]["message"]["content"]
print(result)

const baseUrl = "";

const headers = {
  authorization: "<YOUR_API_KEY>",
  "content-type": "application/json",
};

// Step 1: Transcribe your audio file
const audioUrl = "";

const data = {
  audio_url: audioUrl,
  speech_models: ["universal-3-pro", "universal-2"],
  language_detection: true,
};

const response = await fetch(`${baseUrl}/v2/transcript`, {
  method: "POST",
  headers,
  body: JSON.stringify(data),
});

const { id: transcriptId } = await response.json();
const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

let transcriptionResult;
while (true) {
  const pollingResponse = await fetch(pollingEndpoint, { headers });
  transcriptionResult = await pollingResponse.json();

  if (transcriptionResult.status === "completed") {
    break;
  } else if (transcriptionResult.status === "error") {
    throw new Error(`Transcription failed: ${transcriptionResult.error}`);
  } else {
    await new Promise((resolve) => setTimeout(resolve, 3000));
  }
}

// Step 2: Generate a summary using LLM Gateway
const prompt =
  "Provide a brief summary of the transcript in bullet point format.";

const llmGatewayData = {
  model: "claude-sonnet-4-6",
  messages: [
    {
      role: "user",
      content: `${prompt}\n\nTranscript: ${transcriptionResult.text}`,
    },
  ],
  max_tokens: 1000,
};

const result = await fetch(
  "",
  {
    method: "POST",
    headers,
    body: JSON.stringify(llmGatewayData),
  }
);

const resultData = await result.json();
console.log(resultData.choices[0].message.content);

Example output

- Smoke from hundreds of wildfires in Canada is triggering air quality alerts throughout the US, with skylines from Maine to Maryland to Minnesota appearing gray and smoggy.
- Air pollution levels in Baltimore are considered unhealthy, with exposure to high levels leading to various health problems.
- With climate change driving more wildfires, experts warn that wide-ranging air quality consequences may become more frequent.

Customize your summary

You can control the summary output by adjusting the prompt. Here are some examples:

Bullet point summary

prompt = """Provide a brief summary of the transcript in bullet point format.
Focus on the key points and main takeaways."""

Paragraph summary

prompt = """Provide a concise paragraph summary of the transcript.
Capture the main topics and conclusions."""

Headline summary

prompt = """Provide a single sentence headline that captures the main topic
of the transcript."""

Conversational summary

prompt = """Summarize this conversation between multiple speakers.
Include who said what and the key points each speaker made."""

Custom format

You can define any format you need:

prompt = """Summarize the transcript using the following format:
- Topic: [main topic]
- Key Points: [list of 3-5 key points]
- Action Items: [any action items mentioned]
- Conclusion: [one sentence conclusion]"""

API reference

Step 1: Transcribe audio

curl  \
--header "Authorization: <YOUR_API_KEY>" \
--header "Content-Type: application/json" \
--data '{
  "audio_url": "YOUR_AUDIO_URL"
}'

Poll for the transcript result until the status is completed, then extract the transcript text.

Step 2: Generate summary with LLM Gateway

curl  \
--header "Authorization: <YOUR_API_KEY>" \
--header "Content-Type: application/json" \
--data '{
  "model": "claude-sonnet-4-6",
  "messages": [
    {"role": "user", "content": "Provide a brief summary of the transcript.\n\nTranscript: YOUR_TRANSCRIPT_TEXT"}
  ],
  "max_tokens": 1000
}'

Key	Type	Description
model	string	The LLM model to use. See available models.
messages	array	The messages to send to the model, including your summarization prompt and transcript text.
max_tokens	number	Maximum number of tokens in the response. Adjust based on desired summary length.

Response

{
  "choices": [
    {
      "message": {
        "content": "Your generated summary text..."
      }
    }
  ]
}

Next steps

• LLM Gateway Overview - Learn more about available models and features
• Apply LLM Gateway to pre-recorded audio - General guide for using LLM Gateway with transcripts
• Basic Chat Completions - Learn more about the chat completions API
• Structured Outputs - Constrain responses to a specific JSON schema