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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 = "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, "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\n Transcript: { transcription_result[ 'text' ] } " } ], "max_tokens" : 1000 } 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 (result)
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 , 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\n Transcript: ${ transcriptionResult . text } ` , }, ], max_tokens: 1000 , }; 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 ( 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."""
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 https://api.assemblyai.com/v2/transcript \ --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 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 of the transcript.\n\nTranscript: YOUR_TRANSCRIPT_TEXT"} ], "max_tokens": 1000 }'
Key Type Description modelstring The LLM model to use. See available models . messagesarray The messages to send to the model, including your summarization prompt and transcript text. max_tokensnumber Maximum number of tokens in the response. Adjust based on desired summary length.
Response { "choices" : [ { "message" : { "content" : "Your generated summary text..." } } ] }
Next steps 
