Quickstart

import os
import json
import requests

API_KEY = os.environ.get("AURIGIN_API_KEY", "aurigin_test_1234567890abcdef")
BASE_URL = "https://api.aurigin.ai/v1"
FILE_PATH = "path/to/your/recording.wav"  # Replace with the path to your local audio file

with open(FILE_PATH, "rb") as f:
    files = {
        "file": (os.path.basename(FILE_PATH), f, "audio/wav")
    }
    r2 = requests.post(f"{BASE_URL}/predict", headers={"x-api-key": API_KEY}, files=files)
    print("Multipart response:", r2.status_code, r2.json())
    
    # The response contains predictions for each 5-second chunk of your audio
    # For a 15-second file, you'll get 3 predictions: ["fake", "real", "fake"]

import os
import json
import requests

API_KEY = os.environ.get("AURIGIN_API_KEY", "aurigin_test_1234567890abcdef")
BASE_URL = "https://api.aurigin.ai/v1"
ENROLLMENT_FILE = "path/to/user_voice_enrollment.wav"  # >= 10 seconds

with open(ENROLLMENT_FILE, "rb") as f:
    files = {"audio_file": (os.path.basename(ENROLLMENT_FILE), f, "audio/wav")}
    data = {"user_id": "john_doe"}
    
    response = requests.post(
        f"{BASE_URL}/voiceid/enroll",
        headers={"x-api-key": API_KEY},
        files=files,
        data=data
    )
    
    result = response.json()
    embedding_vector = result["embedding"]
    
    # Store the embedding vector securely for future verification
    print(f"Enrollment successful! Embedding dimension: {len(embedding_vector)}")
    print(f"Model: {result['model_version']}")
    print(f"Processing time: {result['processing_time']:.2f}s")
    
    # Save embedding for later use
    with open("user_voiceprint.json", "w") as out:
        json.dump(embedding_vector, out)

import os
import json
import requests

API_KEY = os.environ.get("AURIGIN_API_KEY", "aurigin_test_1234567890abcdef")
BASE_URL = "https://api.aurigin.ai/v1"
VERIFICATION_FILE = "path/to/verification_sample.wav"

# Load the stored embedding vector
with open("user_voiceprint.json", "r") as f:
    embedding_vector = json.load(f)

with open(VERIFICATION_FILE, "rb") as f:
    files = {"audio_file": (os.path.basename(VERIFICATION_FILE), f, "audio/wav")}
    data = {
        "voice_vector": json.dumps(embedding_vector),
        "user_id": "john_doe"
    }
    
    response = requests.post(
        f"{BASE_URL}/voiceid/verify",
        headers={"x-api-key": API_KEY},
        files=files,
        data=data
    )
    
    result = response.json()
    
    print(f"Is Match: {result['is_match']}")
    print(f"Similarity Score: {result['similarity_score']:.2%}")
    print(f"Confidence Score: {result['confidence_score']:.2%}")
    print(f"Model Version: {result['model_version']}")
    print(f"Processing Time: {result['processing_time']:.2f}s")
    if result['deepfake_score'] is not None:
        print(f"Deepfake Score: {result['deepfake_score']:.2%}")
    
    # Make authentication decision
    if result['is_match'] and result['similarity_score'] >= 0.85:
        print("✅ Authentication successful!")
    else:
        print("❌ Authentication failed - require additional verification")