whisper-large-v3

Brief: Transcribe audio to text using OpenAI whisper-large-v3, a high-accuracy multi-language speech recognition model.

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

whisper-large-v3 is a high-accuracy, multi-language speech recognition model, suitable for real-time transcription and voice interaction applications.

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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" \
  -F "language=zh"

JavaScript (fetch) Example

const formData = new FormData();
formData.append('file', audioFile);
formData.append('model', 'whisper-large-v3');
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',
        '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 transcription result."
}

Note: whisper-large-v3 is suitable for high-accuracy multi-language transcription, especially for voice interaction and real-time scenarios.