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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) | |