Model selection

The speech_models parameter lets you specify which model(s) to use for transcription. If omitted, it defaults to ["universal-3-5-pro", "universal-2"]. With this default, universal-3-5-pro handles its 18 supported languages; for all other languages, the request will automatically fall back to universal-2.

Available models

Python SDK
JavaScript SDK
API

Name	Parameter	Description	Best for
Universal-3.5 Pro Recommended	`speech_models=['universal-3-5-pro']`	Our highest accuracy, fastest model with 18-language support, native code switching, and contextual prompting.	Highest-accuracy transcription, post-call analytics, meeting notetakers, medical transcription, domain-specific accuracy via prompting
Universal-3.6 Pro Preview Preview	`speech_models=['universal-3-6-pro-preview']`	Our next-generation pre-recorded model, currently in preview. Supports 32 languages.	Early access to the next-generation pre-recorded model.
Universal-2	`speech_models=['universal-2']`	Our accurate, cost-effective model with support across 99 languages.	High-volume batch transcription, 99-language coverage, price-sensitive workloads, fallback for unsupported U3 Pro languages

Name	Parameter	Description	Best for
Universal-3.5 Pro Recommended	`speech_models: ['universal-3-5-pro']`	Our highest accuracy, fastest model with 18-language support, native code switching, and contextual prompting.	Highest-accuracy transcription, post-call analytics, meeting notetakers, medical transcription, domain-specific accuracy via prompting
Universal-3.6 Pro Preview Preview	`speech_models: ['universal-3-6-pro-preview']`	Our next-generation pre-recorded model, currently in preview. Supports 32 languages.	Early access to the next-generation pre-recorded model.
Universal-2	`speech_models: ['universal-2']`	Our accurate, cost-effective model with support across 99 languages.	High-volume batch transcription, 99-language coverage, price-sensitive workloads, fallback for unsupported U3 Pro languages

Name	API Parameter	Description	Best for
Universal-3.5 Pro Recommended	`"speech_models": ["universal-3-5-pro"]`	Our highest accuracy, fastest model with 18-language support, native code switching, and contextual prompting.	Highest-accuracy transcription, post-call analytics, meeting notetakers, medical transcription, domain-specific accuracy via prompting
Universal-3.6 Pro Preview Preview	`"speech_models": ["universal-3-6-pro-preview"]`	Our next-generation pre-recorded model, currently in preview. Supports 32 languages.	Early access to the next-generation pre-recorded model.
Universal-2	`"speech_models":["universal-2"]`	Our accurate, cost-effective model with support across 99 languages.	High-volume batch transcription, 99-language coverage, price-sensitive workloads, fallback for unsupported U3 Pro languages

Preview models are subject to change. Preview models are for early testing and evaluation. Before a model becomes stable and generally available, its behavior, output quality, supported features, and API parameters may change without notice.Known limitations of universal-3-6-pro-preview: Punctuation could be less frequent for some European languages (e.g., Danish), and accuracy may drop on complex code-switching audio where the language changes frequently at the word or sentence level. We’re actively working on improvements to both.

Set the model

Python
Python SDK
JavaScript
JavaScript SDK

You can change the model by setting the speech_models in the POST request body:

import requests
import time

base_url = "https://api.assemblyai.com"

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

data = {
    "audio_url": "https://assembly.ai/wildfires.mp3",
    "speech_models": ["universal-3-5-pro"],
    "language_detection": True
}

url = base_url + "/v2/transcript"
response = requests.post(url, 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':
    print(transcription_result['text'])
    break

  elif transcription_result['status'] == 'error':
    raise RuntimeError(f"Transcription failed: {transcription_result['error']}")

  else:
    time.sleep(3)

You can change the model by setting speech_models in the transcription config:

from assemblyai import TranscriptStatus
from assemblyai.prerecorded.v2 import Transcriber, TranscriptionConfig

audio_file = "https://assembly.ai/wildfires.mp3"

config = TranscriptionConfig(
    speech_models=["universal-3-5-pro"],
    language_detection=True
)
transcript = Transcriber(api_key="<YOUR_API_KEY>", config=config).transcribe(audio_file)

if transcript.status == TranscriptStatus.error:
  raise RuntimeError(f"Transcription failed: {transcript.error}")

print(transcript.text)

You can change the model by setting the speech_models in the POST request body:

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

const headers = {
  authorization: "<YOUR_API_KEY>",
};

const data = {
  audio_url: "https://assembly.ai/wildfires.mp3",
  speech_models: ["universal-3-5-pro"],
  language_detection: true,
};

const url = `${baseUrl}/v2/transcript`;
let res = await fetch(url, {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify(data),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const response = await res.json();

const transcriptId = response.id;
const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

while (true) {
  res = await fetch(pollingEndpoint, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  const transcriptionResult = await res.json();

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

You can change the model by setting the speech_models in the transcript parameters:

import { AssemblyAI } from "assemblyai";

const client = new AssemblyAI({
  apiKey: "<YOUR_API_KEY>",
});

const audioFile = "https://assembly.ai/wildfires.mp3";

const params = {
  audio: audioFile,
  speech_models: ["universal-3-5-pro"],
  language_detection: true,
};

const run = async () => {
  const transcript = await client.transcripts.transcribe(params);

  if (transcript.status === "error") {
    throw new Error(`Transcription failed: ${transcript.error}`);
  }

  console.log(transcript.text);
};

run();

Transcribe all 99 languages by combining speech models

By default, speech_models is set to ["universal-3-5-pro", "universal-2"]. AssemblyAI uses the first model in the list that supports the audio’s language, whether detected or set with language_code.

If the language is one of the 18 languages supported by Universal-3.5 Pro, the audio is transcribed with universal-3-5-pro.
Otherwise, AssemblyAI falls back to universal-2.

Identify the model used

After transcription completes, you can check which model was actually used to process your request by reading the speech_model_used field. This is useful when you provide multiple models in the speech_models array, as the system may fall back to a different model depending on language support.

Python
Python SDK
JavaScript
JavaScript SDK

import requests
import time

base_url = "https://api.assemblyai.com"

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

data = {
    "audio_url": "https://assembly.ai/wildfires.mp3",
    "speech_models": ["universal-3-5-pro"],
    "language_detection": True
}

url = base_url + "/v2/transcript"
response = requests.post(url, 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':
    print(f"Model used: {transcription_result['speech_model_used']}")
    print(transcription_result['text'])
    break

  elif transcription_result['status'] == 'error':
    raise RuntimeError(f"Transcription failed: {transcription_result['error']}")

  else:
    time.sleep(3)

from assemblyai import TranscriptStatus
from assemblyai.prerecorded.v2 import Transcriber, TranscriptionConfig

audio_file = "https://assembly.ai/wildfires.mp3"

config = TranscriptionConfig(
    speech_models=["universal-3-5-pro"],
    language_detection=True
)
transcript = Transcriber(api_key="<YOUR_API_KEY>", config=config).transcribe(audio_file)

if transcript.status == TranscriptStatus.error:
    raise RuntimeError(f"Transcription failed: {transcript.error}")

print(f"Model used: {transcript.json_response['speech_model_used']}")
print(transcript.text)

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

const headers = {
  authorization: "<YOUR_API_KEY>",
};

const data = {
  audio_url: "https://assembly.ai/wildfires.mp3",
  speech_models: ["universal-3-5-pro"],
  language_detection: true,
};

const url = `${baseUrl}/v2/transcript`;
let res = await fetch(url, {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify(data),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const response = await res.json();

const transcriptId = response.id;
const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

while (true) {
  res = await fetch(pollingEndpoint, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  const transcriptionResult = await res.json();

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

import { AssemblyAI } from "assemblyai";

const client = new AssemblyAI({
  apiKey: "<YOUR_API_KEY>",
});

const audioFile = "https://assembly.ai/wildfires.mp3";

const params = {
  audio: audioFile,
  speech_models: ["universal-3-5-pro"],
  language_detection: true,
};

const run = async () => {
  const transcript = await client.transcripts.transcribe(params);

  if (transcript.status === "error") {
    throw new Error(`Transcription failed: ${transcript.error}`);
  }

  console.log(`Model used: ${transcript.speech_model_used}`);
  console.log(transcript.text);
};

run();

Complete example

Here is the full working code that demonstrates model selection with error handling:

Python
Python SDK
JavaScript
JavaScript SDK

import requests
import time

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

data = {
    "audio_url": "https://assembly.ai/wildfires.mp3",
    "speech_models": ["universal-3-5-pro"],
    "language_detection": True
}

response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)

if response.status_code != 200:
    print(f"Error: {response.status_code}, Response: {response.text}")
    response.raise_for_status()

transcript_json = response.json()
transcript_id = transcript_json["id"]
polling_endpoint = f"{base_url}/v2/transcript/{transcript_id}"

while True:
    transcript = requests.get(polling_endpoint, headers=headers).json()
    if transcript["status"] == "completed":
        print(f"Model used: {transcript['speech_model_used']}")
        print(f"\nTranscript:\n\n{transcript['text']}")
        break
    elif transcript["status"] == "error":
        raise RuntimeError(f"Transcription failed: {transcript['error']}")
    else:
        time.sleep(3)

from assemblyai import TranscriptStatus
from assemblyai.prerecorded.v2 import Transcriber, TranscriptionConfig

audio_file = "https://assembly.ai/wildfires.mp3"

config = TranscriptionConfig(
    speech_models=["universal-3-5-pro"],
    language_detection=True,
)

transcript = Transcriber(api_key="<YOUR_API_KEY>", config=config).transcribe(audio_file)

if transcript.status == TranscriptStatus.error:
    raise RuntimeError(f"Transcription failed: {transcript.error}")

print(f"Model used: {transcript.json_response['speech_model_used']}")
print(f"\nTranscript:\n\n{transcript.text}")

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

const headers = {
  authorization: "<YOUR_API_KEY>",
};

async function transcribe() {
  const audioFile = "https://assembly.ai/wildfires.mp3";

  const data = {
    audio_url: audioFile,
    speech_models: ["universal-3-5-pro"],
    language_detection: true,
  };

  let res = await fetch(`${baseUrl}/v2/transcript`, {
    method: "POST",
    headers: { ...headers, "Content-Type": "application/json" },
    body: JSON.stringify(data),
  });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  const transcriptResponse = await res.json();
  const transcriptId = transcriptResponse.id;
  const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

  while (true) {
    res = await fetch(pollingEndpoint, { headers });
    if (!res.ok) throw new Error(`Error: ${res.status}`);
    const transcript = await res.json();

    if (transcript.status === "completed") {
      console.log(`Model used: ${transcript.speech_model_used}`);
      console.log(`\nTranscript:\n\n${transcript.text}`);
      break;
    } else if (transcript.status === "error") {
      throw new Error(`Transcription failed: ${transcript.error}`);
    } else {
      await new Promise((resolve) => setTimeout(resolve, 3000));
    }
  }
}

transcribe();

import { AssemblyAI } from "assemblyai";

const client = new AssemblyAI({
  apiKey: "<YOUR_API_KEY>",
});

const audioFile = "https://assembly.ai/wildfires.mp3";

const params = {
  audio: audioFile,
  speech_models: ["universal-3-5-pro"],
  language_detection: true,
};

const run = async () => {
  const transcript = await client.transcripts.transcribe(params);

  if (transcript.status === "error") {
    throw new Error(`Transcription failed: ${transcript.error}`);
  }

  console.log(`Model used: ${transcript.speech_model_used}`);
  console.log(`\nTranscript:\n\n${transcript.text}`);
};

run();

Getting started

Features

API reference

Advanced

Guides

Available models

Set the model

Transcribe all 99 languages by combining speech models

Identify the model used

Complete example

​Available models

​Set the model

​Transcribe all 99 languages by combining speech models

​Identify the model used

​Complete example

Available models

Set the model

Transcribe all 99 languages by combining speech models

Identify the model used

Complete example