whisper-large-v3-turbo

Brief: Transcribe audio to text using OpenAI whisper-large-v3-turbo, optimized for low-latency multi-language recognition.

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Overview

• Method: POST
• Path: /v1/audio/transcriptions
• Content-Type: multipart/form-data

Authentication

• Header: Authorization: Bearer <token>
• Supports bearer token authentication

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Request Body Parameters

Parameter	Type	Required	Description
file	file	Yes	Audio file object, supporting flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, webm. Max file size 25 MB
model	string	Yes	Model name, set to whisper-large-v3-turbo
response_format	string	No	Output format. Supported values: json, text, srt, verbose_json, vtt. Default: json
language	string	No	Audio language. Supported values: zh, en, de, es. Use ISO-639-1 codes to improve accuracy

whisper-large-v3-turbo is a high-performance speech recognition model optimized for low latency and large-scale usage.

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curl Example

curl -X POST "https://api.gpt.ge/v1/audio/transcriptions" \
  -H "Authorization: Bearer sk-xxxx" \
  -F "file=@./audio.wav" \
  -F "model=whisper-large-v3-turbo" \
  -F "response_format=json" \
  -F "language=zh"

JavaScript (fetch) Example

const formData = new FormData();
formData.append('file', audioFile);
formData.append('model', 'whisper-large-v3-turbo');
formData.append('response_format', 'json');
formData.append('language', 'zh');

fetch('https://api.gpt.ge/v1/audio/transcriptions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer sk-xxxx'
  },
  body: formData
}).then(r => r.json()).then(console.log);

Python (requests) Example

import requests

with open('audio.wav', 'rb') as f:
    files = {'file': f}
    data = {
        'model': 'whisper-large-v3-turbo',
        'response_format': 'json',
        'language': 'zh'
    }
    response = requests.post(
        'https://api.gpt.ge/v1/audio/transcriptions',
        headers={'Authorization': 'Bearer sk-xxxx'},
        files=files,
        data=data
    )
print(response.json())

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Response Example (200)

{
  "text": "Hello, this is the OpenAI Whisper Large V3 Turbo transcription result."
}

Note: whisper-large-v3-turbo offers a strong balance between low latency and accuracy, making it suitable for real-time transcription.