Speech to text

This standalone API transcribes supplied audio. To ask a model to respond to audio instead, see Audio inputs.

Transcribe audio

Send an audio file to the transcription endpoint when you want text output from a recorded file. If your app acts on behalf of an end user, also send X-On-Behalf-Of. Supported audio formats: FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV, WebM. To save the transcription (and input audio) to history, set store=true.

import { SDK } from '@meetkai/mka1';
import { openAsBlob } from 'node:fs';

const sdk = new SDK({ bearerAuth: 'Bearer <mka1-api-key>' });

const result = await sdk.llm.speech.transcribe({
  xOnBehalfOf: '<end-user-id>', // optional — attribute the request to one of your end users
  language: 'en',
  prompt: 'This is a technical podcast about machine learning.',
  temperature: 0.2,
  store: true,
  requestBody: {
    file: await openAsBlob('episode.wav'),
  },
});

console.log(result.text);
console.log(result.language);
console.log(result.confidence);

from meetkai_mka1 import SDK

sdk = SDK(bearer_auth="Bearer <mka1-api-key>")

result = sdk.llm.speech.transcribe(
    file={"file_name": "episode.wav", "content": open("episode.wav", "rb")},
    language="en",
    prompt="This is a technical podcast about machine learning.",
    temperature=0.2,
    store=True,
)

print(result.text)
print(result.language)
print(result.confidence)

using MeetKai.MKA1;
using MeetKai.MKA1.Types.Components;
using MeetKai.MKA1.Types.Requests;

var sdk = new SDK(bearerAuth: "Bearer <mka1-api-key>");

var result = await sdk.Llm.Speech.TranscribeAsync(new TranscribeRequest()
{
    Language = "en",
    Prompt = "This is a technical podcast about machine learning.",
    Temperature = 0.2,
    Store = true,
    Body = new TranscribeRequestBody()
    {
        File = new TranscribeFile()
        {
            FileName = "episode.wav",
            Content = System.IO.File.ReadAllBytes("episode.wav"),
        },
    },
});

Console.WriteLine(result.TranscriptionResponse!.Text);
Console.WriteLine(result.TranscriptionResponse!.Language);
Console.WriteLine(result.TranscriptionResponse!.Confidence);

mka1 llm speech transcribe \
  --file ./episode.wav \
  --language en \
  --prompt 'This is a technical podcast about machine learning.' \
  --temperature 0.2 \
  --store \
  -H 'X-On-Behalf-Of: <end-user-id>'

curl 'https://apigw.mka1.com/api/v1/llm/speech/transcriptions?language=en&prompt=This%20is%20a%20technical%20podcast%20about%20machine%20learning.&temperature=0.2&store=true' \
  --request POST \
  --header 'Authorization: Bearer <mka1-api-key>' \
  --header 'X-On-Behalf-Of: <end-user-id>' \
  --form 'file=@episode.wav'

The response includes the transcript text plus detected language and confidence:

{
  "text": "Hello! We're excited to show you our native speech capabilities.",
  "language": "en",
  "confidence": 0.8429018476208717
}

Separate speakers in one transcript

If you need diarization, enable speaker data in the transcription request. When enabled, the response can include a speakers array with speaker-labeled segments and timing metadata.

For include_speaker_data, upload WAV or PCM audio for non-streaming transcription. Other audio formats return 400 BAD_REQUEST with the message Speaker diarization currently requires WAV/PCM audio for non-streaming transcription.

TypeScript SDK

const result = await sdk.llm.speech.transcribe({
  xOnBehalfOf: '<end-user-id>',
  language: 'en',
  includeSpeakerData: true,
  prompt: 'This is a short podcast clip about AI product updates.',
  temperature: 0.2,
  requestBody: {
    file: await openAsBlob('panel.wav'),
  },
});

console.log(result.speakers);

Example response with speaker separation:

{
  "text": "Welcome back to the show. Today we're looking at how speech APIs fit into production apps. We'll keep it practical and focus on latency, accuracy, and speaker turns.",
  "language": "en",
  "confidence": 0.91177404,
  "speakers": [
    {
      "speaker": "Speaker-1",
      "text": "Welcome back to the show.",
      "confidence": 0.91177404,
      "offset_ms": 80,
      "duration_ms": 1280
    },
    {
      "speaker": "Speaker-2",
      "text": "Today we're looking at how speech APIs fit into production apps.",
      "confidence": 0.91177404,
      "offset_ms": 1540,
      "duration_ms": 3380
    },
    {
      "speaker": "Speaker-1",
      "text": "We'll keep it practical and focus on latency, accuracy, and speaker turns.",
      "confidence": 0.91177404,
      "offset_ms": 5220,
      "duration_ms": 3660
    }
  ]
}

Use the top-level text field when you need a single merged transcript. Use speakers when you need captions, turn-taking, or downstream speaker analytics.

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Speech to text

Transcribe audio

Separate speakers in one transcript

​Transcribe audio

​Separate speakers in one transcript

Transcribe audio

Separate speakers in one transcript