whisper-large

Brief: Transcribe audio to text using OpenAI whisper-large.

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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
language	string	No	Audio language. Supported values: zh, en, de, es. Use ISO-639-1 codes to improve accuracy

whisper-large is the larger OpenAI Whisper model, suitable for higher-accuracy transcription with multiple languages, accents, and background noise.

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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" \
  -F "language=zh"

JavaScript (fetch) Example

const formData = new FormData();
formData.append('file', audioFile);
formData.append('model', 'whisper-large');
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',
        '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 transcription result."
}

Note: whisper-large is trained on 680,000 hours of multilingual data, suitable for high-accuracy transcription in complex audio scenarios.