> ## Documentation Index > Fetch the complete documentation index at: https://docs.mka1.com/llms.txt > Use this file to discover all available pages before exploring further. # Audio responses > Generate spoken output alongside a Responses API reply. Use this guide for spoken output from a [Response](/docs/responses). To synthesize supplied text directly, see [Text to speech](/docs/text-to-speech). The OpenAI examples use its custom-request method for MKA1 extensions that are not part of the OpenAI Responses types. ## Generate audio (text-to-speech) Request audio output by setting `modalities` to `["text", "audio"]` and specifying a voice and format in the `audio` parameter. The response includes both the text transcript and base64-encoded audio data. ### Audio configuration | Parameter | Options | Default | | --------- | ------------------------------------- | ------- | | `voice` | `alloy` and other voice profiles | `alloy` | | `format` | `wav`, `mp3`, `flac`, `opus`, `pcm16` | `wav` | Audio is synthesized at 24 kHz, 16-bit mono. ```ts TypeScript SDK theme={null} import { SDK } from '@meetkai/mka1'; import { writeFileSync } from 'fs'; import type { ResponseObject } from '@meetkai/mka1/models/components'; const sdk = new SDK({ bearerAuth: 'Bearer ' }); const result = await sdk.llm.responses.create({ xOnBehalfOf: '', // optional — attribute the request to one of your end users responsesCreateRequest: { model: 'auto', input: 'Say hello in a friendly way. Keep it very short.', modalities: ['text', 'audio'], audio: { voice: 'alloy', format: 'wav' }, }, }); // The output includes an output_audio item with base64 data and a transcript ``` ```ts OpenAI SDK theme={null} import OpenAI from 'openai'; import { writeFileSync } from 'fs'; const openai = new OpenAI({ apiKey: '', baseURL: 'https://apigw.mka1.com/api/v1/llm/', defaultHeaders: { 'X-On-Behalf-Of': '' }, }); const response = await openai.post<{ output: Array<{ type: string; data?: string; transcript?: string }> }>('/responses', { body: { model: 'auto', input: 'Say hello in a friendly way. Keep it very short.', modalities: ['text', 'audio'], audio: { voice: 'alloy', format: 'wav' }, stream: false, } }); // Find the audio output const audioItem = response.output.find((item) => item.type === 'output_audio'); // audioItem.data contains base64-encoded WAV // audioItem.transcript contains the spoken text ``` ```python Python SDK theme={null} from meetkai_mka1 import SDK import base64 sdk = SDK(bearer_auth="Bearer ") result = sdk.llm.responses.create( model="auto", input="Say hello in a friendly way. Keep it very short.", modalities=["text", "audio"], audio={"voice": "alloy", "format": "wav"}, ) # The output includes an output_audio item with base64 data and a transcript ``` ```csharp C# SDK theme={null} using MeetKai.MKA1; using MeetKai.MKA1.Types.Components; using MeetKai.MKA1.Types.Requests; var sdk = new SDK(bearerAuth: "Bearer "); var result = await sdk.Llm.Responses.CreateAsync(new ResponsesCreateRequest() { Model = "auto", Input = ResponsesCreateRequestInput.CreateStr("Say hello in a friendly way. Keep it very short."), Modalities = new List { ResponsesCreateRequestModality.Text, ResponsesCreateRequestModality.Audio, }, Audio = new Audio() { Voice = "alloy", Format = ResponsesCreateRequestFormat.Wav, }, }); // The output includes an output_audio item with base64 data and a transcript ``` ```bash CLI theme={null} mka1 llm responses create \ -H 'X-On-Behalf-Of: ' \ --body '{ "model": "auto", "input": "Say hello in a friendly way. Keep it very short.", "modalities": ["text", "audio"], "audio": { "voice": "alloy", "format": "wav" } }' ``` ```bash Bash theme={null} curl https://apigw.mka1.com/api/v1/llm/responses \ --request POST \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer ' \ --header 'X-On-Behalf-Of: ' \ --data '{ "model": "auto", "input": "Say hello in a friendly way. Keep it very short.", "modalities": ["text", "audio"], "audio": { "voice": "alloy", "format": "wav" } }' ``` The response contains an `output_audio` item with the base64-encoded audio and a transcript of what was spoken: ```json theme={null} { "status": "completed", "output": [ { "type": "message", "role": "assistant", "content": [ { "type": "output_text", "text": "Hello!" } ] }, { "type": "output_audio", "id": "audio_460caf1079b34fa0b4aa74448dff4ea7", "data": "", "transcript": "Hi there!", "status": "completed" } ] } ``` The `data` field contains the full audio file (268 KB in this example). The `transcript` field contains the text the model chose to speak — which may differ slightly from the text output. ### Save audio to a file ```ts TypeScript SDK theme={null} const result = await sdk.llm.responses.create({ responsesCreateRequest: { model: 'auto', input: 'Read this sentence aloud: The quick brown fox jumps over the lazy dog.', modalities: ['text', 'audio'], audio: { voice: 'alloy', format: 'mp3' }, }, }) as ResponseObject; // Find the audio output in the response const audioItem = result.output.find((item) => item.type === 'output_audio'); if (audioItem) { const audioBuffer = Buffer.from(audioItem.data, 'base64'); writeFileSync('output.mp3', audioBuffer); } ``` ```ts OpenAI SDK theme={null} const response = await openai.post<{ output: Array<{ type: string; data?: string; transcript?: string }> }>('/responses', { body: { model: 'auto', input: 'Read this sentence aloud: The quick brown fox jumps over the lazy dog.', modalities: ['text', 'audio'], audio: { voice: 'alloy', format: 'mp3' }, stream: false, } }); const audioItem = response.output.find((item) => item.type === 'output_audio'); if (audioItem?.data) { const audioBuffer = Buffer.from(audioItem.data, 'base64'); writeFileSync('output.mp3', audioBuffer); } ``` ```python Python SDK theme={null} result = sdk.llm.responses.create( model="auto", input="Read this sentence aloud: The quick brown fox jumps over the lazy dog.", modalities=["text", "audio"], audio={"voice": "alloy", "format": "mp3"}, ) # Find the audio output in the response for item in result.output: if item.type == "output_audio": audio_bytes = base64.b64decode(item.data) with open("output.mp3", "wb") as f: f.write(audio_bytes) ``` ```csharp C# SDK theme={null} var result = await sdk.Llm.Responses.CreateAsync(new ResponsesCreateRequest() { Model = "auto", Input = ResponsesCreateRequestInput.CreateStr( "Read this sentence aloud: The quick brown fox jumps over the lazy dog."), Modalities = new List { ResponsesCreateRequestModality.Text, ResponsesCreateRequestModality.Audio, }, Audio = new Audio() { Voice = "alloy", Format = ResponsesCreateRequestFormat.Mp3, }, }); // Save the audio output to a file // (iterate result.Output to find the output_audio item and decode its base64 data) ``` ```bash CLI theme={null} # Generate audio and extract the base64 data, then decode to a file mka1 llm responses create \ --body '{ "model": "auto", "input": "Read this sentence aloud: The quick brown fox jumps over the lazy dog.", "modalities": ["text", "audio"], "audio": { "voice": "alloy", "format": "mp3" } }' \ --output-format json \ --jq '.output[] | select(.type == "output_audio") | .data' | base64 -d > output.mp3 ``` ```bash Bash theme={null} # Generate audio and extract the base64 data, then decode to a file curl -s https://apigw.mka1.com/api/v1/llm/responses \ --request POST \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer ' \ --data '{ "model": "auto", "input": "Read this sentence aloud: The quick brown fox jumps over the lazy dog.", "modalities": ["text", "audio"], "audio": { "voice": "alloy", "format": "mp3" } }' | jq -r '.output[] | select(.type == "output_audio") | .data' | base64 -d > output.mp3 ``` ### Supported languages Audio output supports automatic language detection and 20+ languages including English, Chinese, Hindi, Spanish, Arabic, Bengali, Portuguese, Russian, Japanese, Punjabi, German, Korean, French, Turkish, Italian, Thai, Polish, Dutch, Indonesian, Vietnamese, and Urdu. For dedicated speech synthesis, see [Text to speech](/docs/text-to-speech).