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import joblib
from transformers import AutoFeatureExtractor, WavLMModel
import torch
import soundfile as sf
import numpy as np
import gradio as gr
import librosa
class HuggingFaceFeatureExtractor:
def __init__(self, model_class, name):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.feature_extractor = AutoFeatureExtractor.from_pretrained(name)
self.model = model_class.from_pretrained(name)
self.model.eval()
self.model.to(self.device)
def __call__(self, audio, sr):
inputs = self.feature_extractor(
audio,
sampling_rate=sr,
return_tensors="pt",
padding=True,
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.model(**inputs)
return outputs.last_hidden_state
FEATURE_EXTRACTORS = {
"wavlm-base": lambda: HuggingFaceFeatureExtractor(WavLMModel, "microsoft/wavlm-base"),
"wavLM-V1": lambda: HuggingFaceFeatureExtractor(WavLMModel, "DavidCombei/wavLM-base-Deepfake_V1"),
"wavLM-V2": lambda: HuggingFaceFeatureExtractor(WavLMModel, "DavidCombei/wavLM-base-Deepfake_V2"),
"wavLM-V3": lambda: HuggingFaceFeatureExtractor(WavLMModel, "DavidCombei/wavLM-base-Deepfake_V3"),
}
model1 = joblib.load('model1.joblib')
model2 = joblib.load('model2.joblib')
model3 = joblib.load('model3.joblib')
model4 = joblib.load('model4.joblib')
final_model = joblib.load('final_model.joblib')
def process_audio(file_audio):
audio, sr = librosa.load(file_audio, sr=16000) # Resample to 16 kHz
if len(audio.shape) > 1:
audio = audio[0]
extractor_1 = FEATURE_EXTRACTORS['wavlm-base']()
extractor_2 = FEATURE_EXTRACTORS['wavLM-V1']()
extractor_3 = FEATURE_EXTRACTORS['wavLM-V2']()
extractor_4 = FEATURE_EXTRACTORS['wavLM-V3']()
eval1 = extractor_1(audio, sr)
eval1 = torch.mean(eval1, dim=1).cpu().numpy()
eval2 = extractor_2(audio, sr)
eval2 = torch.mean(eval2, dim=1).cpu().numpy()
eval3 = extractor_3(audio, sr)
eval3 = torch.mean(eval3, dim=1).cpu().numpy()
eval4 = extractor_4(audio, sr)
eval4 = torch.mean(eval4, dim=1).cpu().numpy()
eval1 = eval1.reshape(1, -1)
eval2 = eval2.reshape(1, -1)
eval3 = eval3.reshape(1, -1)
eval4 = eval4.reshape(1, -1)
eval_prob1 = model1.predict_proba(eval1)[:, 1].reshape(-1, 1)
eval_prob2 = model2.predict_proba(eval2)[:, 1].reshape(-1, 1)
eval_prob3 = model3.predict_proba(eval3)[:, 1].reshape(-1, 1)
eval_prob4 = model4.predict_proba(eval4)[:, 1].reshape(-1, 1)
eval_combined_probs = np.hstack((eval_prob1, eval_prob2, eval_prob3, eval_prob4))
final_prob = final_model.predict_proba(eval_combined_probs)[:, 1]
if final_prob < 0.5:
return f"Fake with a confidence of: {100 - final_prob[0] * 100:.2f}%"
else:
return f"Real with a confidence of: {final_prob[0] * 100:.2f}%"
interface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="filepath"),
outputs="text",
title="Audio Deepfake Detection",
description="Upload an audio file to detect whether it is fake or real. The system uses features ensamble from wavLM base and finetuned versions. Submitted to ASVSpoof5.",
)
interface.launch(share=True)
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