Auto Chapters - AssemblyAI

Supported regions

US & EU

Generate chapter summaries from your audio transcripts using LLM Gateway. This approach gives you full control over how chapters are created and summarized.

The auto_chapters parameter on the transcription API is deprecated. Use LLM Gateway as shown below for more flexible and powerful chapter summaries.

Quickstart

Python
JavaScript

import requests
import time

base_url = "https://api.assemblyai.com"
headers = {"authorization": "<YOUR_API_KEY>"}

# Step 1: Transcribe your audio file
audio_url = "https://assembly.ai/wildfires.mp3"

data = {
    "audio_url": audio_url,
    "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: Get paragraphs from the transcript
paragraphs = requests.get(polling_endpoint + '/paragraphs', headers=headers).json()['paragraphs']

# Step 3: Combine paragraphs into groups for chapter summaries
combined_paragraphs = []
step = 2  # Adjust to control chapter length

for i in range(0, len(paragraphs), step):
    paragraph_group = paragraphs[i : i + step]
    start = paragraph_group[0]['start']
    end = paragraph_group[-1]['end']
    text = " ".join(p['text'] for p in paragraph_group)
    combined_paragraphs.append({"text": text, "start": start, "end": end})

# Step 4: Generate chapter summaries with LLM Gateway
for chapter in combined_paragraphs:
    llm_gateway_data = {
        "model": "claude-sonnet-4-6",
        "messages": [
            {"role": "user", "content": f"Provide a brief one-paragraph summary, a one-line gist, and a headline for this section of a transcript.\n\nText: {chapter['text']}"}
        ],
        "max_tokens": 500
    }

    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers=headers,
        json=llm_gateway_data
    )

    result = response.json()["choices"][0]["message"]["content"]
    print(f"{chapter['start']}-{chapter['end']}: {result}\n")

const baseUrl = "https://api.assemblyai.com";

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

// Step 1: Transcribe your audio file
const audioUrl = "https://assembly.ai/wildfires.mp3";

const data = {
  audio_url: audioUrl,
  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: Get paragraphs from the transcript
const paragraphsResponse = await fetch(`${pollingEndpoint}/paragraphs`, {
  headers,
});
const { paragraphs } = await paragraphsResponse.json();

// Step 3: Combine paragraphs into groups for chapter summaries
const step = 2; // Adjust to control chapter length
const combinedParagraphs = [];

for (let i = 0; i < paragraphs.length; i += step) {
  const group = paragraphs.slice(i, i + step);
  combinedParagraphs.push({
    text: group.map((p) => p.text).join(" "),
    start: group[0].start,
    end: group[group.length - 1].end,
  });
}

// Step 4: Generate chapter summaries with LLM Gateway
for (const chapter of combinedParagraphs) {
  const llmGatewayData = {
    model: "claude-sonnet-4-6",
    messages: [
      {
        role: "user",
        content: `Provide a brief one-paragraph summary, a one-line gist, and a headline for this section of a transcript.\n\nText: ${chapter.text}`,
      },
    ],
    max_tokens: 500,
  };

  const result = await fetch(
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
    {
      method: "POST",
      headers,
      body: JSON.stringify(llmGatewayData),
    }
  );

  const resultData = await result.json();
  console.log(
    `${chapter.start}-${chapter.end}: ${resultData.choices[0].message.content}\n`
  );
}

Example output

240-60890: 
Headline: Canadian Wildfire Smoke Triggers Air Quality Alerts Across the US
Gist: Wildfire smoke affects US air quality
Summary: Smoke from hundreds of wildfires in Canada is causing hazy conditions and air quality alerts in multiple states. Peter DeCarlo, an environmental health expert from Johns Hopkins University, explains that dry conditions and specific weather patterns are channeling the smoke southward, affecting the mid-Atlantic and Northeast regions.

62270-113214:
Headline: Baltimore Air Quality Reaches Unhealthy Levels Due to Particulate Matter
Gist: Dangerous particulate matter levels in Baltimore
Summary: The air quality in Baltimore has reached unhealthy levels due to high concentrations of particulate matter. These microscopic particles can affect respiratory, cardiovascular, and neurological systems, measuring 150 micrograms per cubic meter—10 times higher than the annual average.

Customize your chapters

You can adjust the chapter generation by modifying the step variable to control how many paragraphs are grouped into each chapter, and by customizing the prompt.

Structured chapter output

For a more structured output, use Structured Outputs or specify a JSON format in your prompt:

prompt = """For this section of a transcript, provide the following in JSON format:
{
  "headline": "A single sentence headline",
  "gist": "A few words summarizing the section",
  "summary": "A one paragraph summary"
}

Text: """ + chapter['text']

API reference

Step 1: Transcribe audio

curl https://api.assemblyai.com/v2/transcript \
--header "Authorization: <YOUR_API_KEY>" \
--header "Content-Type: application/json" \
--data '{
  "audio_url": "YOUR_AUDIO_URL"
}'

Step 2: Get paragraphs

Once the transcript is complete, fetch the paragraphs:

curl https://api.assemblyai.com/v2/transcript/YOUR_TRANSCRIPT_ID/paragraphs \
--header "Authorization: <YOUR_API_KEY>"

Step 3: Generate chapter summaries with LLM Gateway

curl https://llm-gateway.assemblyai.com/v1/chat/completions \
--header "Authorization: <YOUR_API_KEY>" \
--header "Content-Type: application/json" \
--data '{
  "model": "claude-sonnet-4-6",
  "messages": [
    {"role": "user", "content": "Provide a brief summary, gist, and headline for this section.\n\nText: YOUR_PARAGRAPH_TEXT"}
  ],
  "max_tokens": 500
}'

Key	Type	Description
`model`	string	The LLM model to use. See available models.
`messages`	array	The messages to send to the model, including your prompt and paragraph text.
`max_tokens`	number	Maximum number of tokens in the response.

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
Structured Outputs - Constrain responses to a specific JSON schema

​Quickstart

​Example output

​Customize your chapters

​Structured chapter output

​API reference

​Step 1: Transcribe audio

​Step 2: Get paragraphs

​Step 3: Generate chapter summaries with LLM Gateway

​Next steps

Quickstart

Example output

Customize your chapters

Structured chapter output

API reference

Step 1: Transcribe audio

Step 2: Get paragraphs

Step 3: Generate chapter summaries with LLM Gateway

Next steps