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Multichannel Transcription

For the complete documentation index, see llms.txt

cURL quickstart — Multichannel Transcription

# Submit transcription with multichannel enabled
curl  \
  --header "Authorization: YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "audio_url": "",
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": true,
    "multichannel": true
  }'

# Poll for result (replace TRANSCRIPT_ID)
curl  \
  --header "Authorization: YOUR_API_KEY"

The response includes audio_channels and utterances with per-channel speaker labels. Add "speaker_labels": true for per-channel diarization.

Multichannel transcription is supported for all languages, regions, and models.

If you have a multichannel audio file with multiple speakers, you can transcribe each of them separately.

The response includes an audio_channels property with the number of different channels, and an additional utterances property, containing a list of turn-by-turn utterances.

Each utterance contains channel information, starting at 1.

Additionally, each word in the words array contains the channel identifier.

Quickstart

import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

# audio_file = "./local_file.mp3"
audio_file = ""

config = aai.TranscriptionConfig(
  speech_models=["universal-3-pro", "universal-2"],
  language_detection=True,
  multichannel=True
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

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

for utterance in transcript.utterances:
  print(f"Channel {utterance.speaker}: {utterance.text}")

import requests
import time

base_url = ""

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

with open("./my-audio.mp3", "rb") as f:
  response = requests.post(base_url + "/v2/upload",
                          headers=headers,
                          data=f)

upload_url = response.json()["upload_url"]

data = {
    "audio_url": upload_url, # You can also use a URL to an audio or video file on the web
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": True,
    "multichannel": 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"Transcript ID: {transcript_id}")
    break

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

  else:
    time.sleep(3)

for utterance in transcription_result['utterances']:
  print(f"Channel {utterance['speaker']}: {utterance['text']}")

import { AssemblyAI } from "assemblyai";

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

// const audioFile = './local_file.mp3'
const audioFile = "";

const params = {
  audio: audioFile,
  speech_models: ["universal-3-pro", "universal-2"],
  language_detection: true,
  multichannel: true,
};

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

  for (const utterance of transcript.utterances ?? []) {
    console.log(`Channel ${utterance.speaker}: ${utterance.text}`);
  }
};

run();

import fs from "fs-extra";

const baseUrl = "";

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

const path = "./my-audio.mp3";
const audioData = await fs.readFile(path);
let res = await fetch(`${baseUrl}/v2/upload`, {
  method: "POST",
  headers,
  body: audioData,
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const uploadResponse = await res.json();
const uploadUrl = uploadResponse.upload_url;

const data = {
  audio_url: uploadUrl, // You can also use a URL to an audio or video file on the web
  speech_models: ["universal-3-pro", "universal-2"],
  language_detection: true,
  multichannel: true,
};

const url = `${baseUrl}/v2/transcript`;
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") {
    for (const utterance of transcriptionResult.utterances) {
      console.log(`Channel ${utterance.speaker}: ${utterance.text}`);
    }
    break;
  } else if (transcriptionResult.status === "error") {
    throw new Error(`Transcription failed: ${transcriptionResult.error}`);
  } else {
    await new Promise((resolve) => setTimeout(resolve, 3000));
  }
}

Multichannel audio increases the transcription time by approximately 40%.

Per-channel diarization

If you have a multichannel audio file where individual channels may contain multiple speakers, you can combine multichannel and speaker_labels to perform diarization within each channel.

When using multichannel with speaker_labels, the speaker_options parameters (min_speakers_expected and max_speakers_expected) are applied per channel, not globally across the entire file. For example, setting min_speakers_expected: 5 and max_speakers_expected: 7 on a 5-channel file means the model will find 5–7 speakers on each channel, resulting in 25–35 total speakers. Adjust your speaker options accordingly when using multichannel transcription.

When both parameters are enabled:

• Channels are labeled numerically (1, 2, 3, etc.)
• Speakers within each channel are labeled alphabetically (A, B, C, etc.)
• The combined speaker label format is {channel}{speaker} (e.g., "1A", "1B", "2A")

For example, if channel 1 has two speakers and channel 2 has one speaker, the labels would be:

• First speaker on channel 1: 1A
• Second speaker on channel 1: 1B
• First speaker on channel 2: 2A

import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

# audio_file = "./local_file.mp3"
audio_file = ""

config = aai.TranscriptionConfig(
  speech_models=["universal-3-pro", "universal-2"],
  language_detection=True,
  multichannel=True,
  speaker_labels=True
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

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

for utterance in transcript.utterances:
  print(f"Speaker {utterance.speaker}: {utterance.text}")

import requests
import time

base_url = ""

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

with open("./my-audio.mp3", "rb") as f:
  response = requests.post(base_url + "/v2/upload",
                          headers=headers,
                          data=f)

upload_url = response.json()["upload_url"]

data = {
    "audio_url": upload_url, # You can also use a URL to an audio or video file on the web
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": True,
    "multichannel": True,
    "speaker_labels": 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"Transcript ID: {transcript_id}")
    break

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

  else:
    time.sleep(3)

for utterance in transcription_result['utterances']:
  print(f"Speaker {utterance['speaker']}: {utterance['text']}")

import { AssemblyAI } from "assemblyai";

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

// const audioFile = './local_file.mp3'
const audioFile = "";

const params = {
  audio: audioFile,
  speech_models: ["universal-3-pro", "universal-2"],
  language_detection: true,
  multichannel: true,
  speaker_labels: true,
};

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

  for (const utterance of transcript.utterances ?? []) {
    console.log(`Speaker ${utterance.speaker}: ${utterance.text}`);
  }
};

run();

import fs from "fs-extra";

const baseUrl = "";

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

const path = "./my-audio.mp3";
const audioData = await fs.readFile(path);
let res = await fetch(`${baseUrl}/v2/upload`, {
  method: "POST",
  headers,
  body: audioData,
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const uploadResponse = await res.json();
const uploadUrl = uploadResponse.upload_url;

const data = {
  audio_url: uploadUrl, // You can also use a URL to an audio or video file on the web
  speech_models: ["universal-3-pro", "universal-2"],
  language_detection: true,
  multichannel: true,
  speaker_labels: true,
};

const url = `${baseUrl}/v2/transcript`;
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") {
    for (const utterance of transcriptionResult.utterances) {
      console.log(`Speaker ${utterance.speaker}: ${utterance.text}`);
    }
    break;
  } else if (transcriptionResult.status === "error") {
    throw new Error(`Transcription failed: ${transcriptionResult.error}`);
  } else {
    await new Promise((resolve) => setTimeout(resolve, 3000));
  }
}