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60d70d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | import io
import librosa
import numpy as np
import soundfile as sf
import torch
from transformers import pipeline
class AudioDetector:
def __init__(self):
print("--- [AudioDetector] Initializing 4-Model Ensemble System... ---")
# The Committee of Experts
self.models_config = [
{
"id": "MelodyMachine/Deepfake-audio-detection-V2",
"name": "MelodyMachine",
"weight": 1.0
},
{
"id": "mo-thecreator/Deepfake-audio-detection",
"name": "Mo-Creator",
"weight": 1.0
},
{
"id": "Hemgg/Deepfake-audio-detection",
"name": "Hemgg",
"weight": 1.0
},
{
"id": "Gustking/wav2vec2-large-xlsr-deepfake-audio-classification",
"name": "Gustking-XLSR",
"weight": 1.2 # Higher weight for the large model
}
]
self.pipelines = []
for cfg in self.models_config:
try:
print(f"--- Loading Model: {cfg['name']} ({cfg['id']}) ---")
# Load pipeline
p = pipeline("audio-classification", model=cfg['id'])
self.pipelines.append({"pipe": p, "config": cfg})
print(f"[+] Loaded {cfg['name']}")
except Exception as e:
print(f"[-] Failed to load {cfg['name']}: {e}")
if not self.pipelines:
print("CRITICAL: No models could be loaded. Ensemble is empty.")
def analyze_audio(self, audio_data: bytes, language: str):
try:
# 1. Load Audio
buffer = io.BytesIO(audio_data)
y, sr = librosa.load(buffer, sr=16000)
# 2. Extract Features (For Explanation Context Only)
# We preserve this for generating professional justifications,
# but the DECISION is purely model-based.
centroid = np.mean(librosa.feature.spectral_centroid(y=y, sr=sr))
# 3. Running The Ensemble
votes = []
total_score = 0
total_weight = 0
print(f"\n--- Running Ensemble Inference on {len(self.pipelines)} models ---")
for item in self.pipelines:
p = item['pipe']
cfg = item['config']
weight = cfg['weight']
try:
# Run Inference
results = p(y, top_k=None) # Get all labels
# Parsing Result for AI Probability
ai_score = 0.0
# Logic: Find the label that means "Fake"
ai_labels = ["fake", "spoof", "aivoice", "artificial", "generated"]
found = False
for r in results:
label_clean = r['label'].lower().strip()
if label_clean in ai_labels:
ai_score = r['score']
found = True
break
# Note: If no AI label is found (e.g. only 'real'/'human'), ai_score stays 0.0 (Human)
# This logic covers {0: 'real', 1: 'fake'} where 'fake' is present.
verdict = "AI" if ai_score > 0.5 else "HUMAN"
# Weighted contribution
votes.append({
"name": cfg['name'],
"ai_prob": ai_score,
"verdict": verdict
})
total_score += (ai_score * weight)
total_weight += weight
print(f" > {cfg['name']}: {ai_score:.4f} ({verdict})")
except Exception as e:
print(f"Error inferencing {cfg['name']}: {e}")
# 4. Final Aggregation
if total_weight > 0:
final_ensemble_score = total_score / total_weight
else:
final_ensemble_score = 0.0 # Fail safe
is_ai = final_ensemble_score > 0.5
final_classification = "AI_GENERATED" if is_ai else "HUMAN"
# Confidence Score: Distance from 0.5, normalized to 0.5-1.0 roughly,
# or just probability of the winning class.
class_confidence = final_ensemble_score if is_ai else (1.0 - final_ensemble_score)
print(f"--- Final Ensemble Score: {final_ensemble_score:.4f} => {final_classification} (Conf: {class_confidence:.2f}) ---\n")
# 5. Construct Explanation
# "3 out of 4 models detected deepfake artifacts..."
ai_votes_count = sum(1 for v in votes if v['verdict'] == 'AI')
total_models = len(votes)
explanations = []
explanations.append(f"Ensemble Analysis: {ai_votes_count}/{total_models} models flagged this audio as AI-generated.")
explanations.append(f"Aggregated Score: {final_ensemble_score*100:.1f}%.")
if is_ai:
if centroid > 2000:
explanations.append("High-frequency spectral artifacts consistent with neural vocoders detected.")
else:
explanations.append("Deep learning pattern matching identified non-biological features.")
else:
explanations.append("Acoustic analysis confirms natural vocal resonance and organic production.")
final_explanation = " ".join(explanations)
return {
"classification": final_classification,
# Return logical confidence (prob of the chosen class)
"confidenceScore": round(float(class_confidence), 2),
"explanation": final_explanation
}
except Exception as e:
print(f"Analysis Failed: {e}")
return {
"classification": "HUMAN", # Fail safe
"confidenceScore": 0.0,
"error": str(e),
"explanation": "Analysis failed due to internal error."
}
# Global Instance
detector = AudioDetector()
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