Skip to content
Primary navigation

Suggested
API Dashboard
API Reference
Audio
Translations

Create translation

client.audio.translations.create(TranslationCreateParams { file, model, prompt, 2 more } body, RequestOptionsoptions?): TranslationCreateResponse
POST/audio/translations

Translates audio into English.

ParametersExpand Collapse
body: TranslationCreateParams { file, model, prompt, 2 more }
file: Uploadable

The audio file object (not file name) translate, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.

model: (string & {}) | AudioModel

ID of the model to use. Only whisper-1 (which is powered by our open source Whisper V2 model) is currently available.

One of the following:
(string & {})
AudioModel = "whisper-1" | "gpt-4o-transcribe" | "gpt-4o-mini-transcribe" | 2 more
One of the following:
"whisper-1"
"gpt-4o-transcribe"
"gpt-4o-mini-transcribe"
"gpt-4o-mini-transcribe-2025-12-15"
"gpt-4o-transcribe-diarize"
prompt?: string

An optional text to guide the model's style or continue a previous audio segment. The prompt should be in English.

response_format?: "json" | "text" | "srt" | 2 more

The format of the output, in one of these options: json, text, srt, verbose_json, or vtt.

One of the following:
"json"
"text"
"srt"
"verbose_json"
"vtt"
temperature?: number

The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

ReturnsExpand Collapse
TranslationCreateResponse = Translation { text } | TranslationVerbose { duration, language, text, segments }
One of the following:
Translation { text }
text: string
TranslationVerbose { duration, language, text, segments }
duration: number

The duration of the input audio.

language: string

The language of the output translation (always english).

text: string

The translated text.

segments?: Array<TranscriptionSegment { id, avg_logprob, compression_ratio, 7 more } >

Segments of the translated text and their corresponding details.

id: number

Unique identifier of the segment.

avg_logprob: number

Average logprob of the segment. If the value is lower than -1, consider the logprobs failed.

formatfloat
compression_ratio: number

Compression ratio of the segment. If the value is greater than 2.4, consider the compression failed.

formatfloat
end: number

End time of the segment in seconds.

formatfloat
no_speech_prob: number

Probability of no speech in the segment. If the value is higher than 1.0 and the avg_logprob is below -1, consider this segment silent.

formatfloat
seek: number

Seek offset of the segment.

start: number

Start time of the segment in seconds.

formatfloat
temperature: number

Temperature parameter used for generating the segment.

formatfloat
text: string

Text content of the segment.

tokens: Array<number>

Array of token IDs for the text content.

Create translation

import fs from "fs";
import OpenAI from "openai";

const openai = new OpenAI();

async function main() {
    const translation = await openai.audio.translations.create({
        file: fs.createReadStream("speech.mp3"),
        model: "whisper-1",
    });

    console.log(translation.text);
}
main();

{
  "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}
Returns Examples
{
  "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}