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🔥 TEST YOUR AUDIO MODEL FROM HUGGING FACE
Simma7/audio_model
"""
import torch
import librosa
from transformers import AutoProcessor, AutoModel
from huggingface_hub import snapshot_download
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# ================= DOWNLOAD MODEL =================
print("⬇️ Downloading model from Hugging Face...")
model_dir = snapshot_download("Simma7/audio_model")
print("✅ Download complete")
# ================= LOAD MODEL =================
print("🧠 Loading model...")
processor = AutoProcessor.from_pretrained(model_dir)
model = AutoModel.from_pretrained(model_dir).to(DEVICE)
print("✅ Model loaded")
# ================= PREDICT =================
def predict(audio_path):
audio, sr = librosa.load(audio_path, sr=16000)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
with torch.no_grad():
outputs = model(**inputs.to(DEVICE))
# embedding-based score
embedding = outputs.last_hidden_state.mean(dim=1)
prob = torch.sigmoid(embedding.mean()).item()
return prob
# ================= MAIN =================
if __name__ == "__main__":
audio_path = "test.wav" # 🔥 put your audio file
print("\n🔍 Analyzing audio...")
prob = predict(audio_path)
if prob > 0.5:
print("\n🔴 FAKE AUDIO")
else:
print("\n🟢 REAL AUDIO")
print(f"📊 Confidence: {prob:.4f}") |