Delete app.py

app.py

@@ -1,89 +0,0 @@
-import joblib
-from transformers import AutoFeatureExtractor, Wav2Vec2Model
-import torch
-import librosa
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-import gradio as gr
-import os
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-class CustomWav2Vec2Model(Wav2Vec2Model):
-    def __init__(self, config):
-        super().__init__(config)
-        self.encoder.layers = self.encoder.layers[:9]
-
-truncated_model = CustomWav2Vec2Model.from_pretrained("facebook/wav2vec2-xls-r-2b")
-
-class HuggingFaceFeatureExtractor:
-    def __init__(self, model, feature_extractor_name):
-        self.device = device
-        self.feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_name)
-        self.model = model
-        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, output_hidden_states=True)
-        return outputs.hidden_states[9]
-
-FEATURE_EXTRACTOR = HuggingFaceFeatureExtractor(truncated_model, "facebook/wav2vec2-xls-r-2b")
-classifier = joblib.load('logreg_margin_pruning_ALL_best.joblib')
-
-def segment_audio(audio, sr, segment_duration):
-    segment_samples = int(segment_duration * sr)
-    total_samples = len(audio)
-    segments = [audio[i:i + segment_samples] for i in range(0, total_samples, segment_samples)]
-    return segments
-
-def process_audio(input_data, segment_duration=10):
-    audio, sr = librosa.load(input_data, sr=16000)
-    if len(audio.shape) > 1:
-        audio = audio[0]
-    segments = segment_audio(audio, sr, segment_duration)
-    segment_predictions = []
-    output_lines = []
-    eer_threshold = 0.985
-    for idx, segment in enumerate(segments):
-        features = FEATURE_EXTRACTOR(segment, sr)
-        features_avg = torch.mean(features, dim=1).cpu().numpy()
-        features_avg = features_avg.reshape(1, -1)
-        probability = classifier.predict_proba(features_avg)[0, 1]
-        if probability >= eer_threshold:
-            pred = 1
-            confidence_percentage = probability * 100
-        else:
-            pred = 0
-            confidence_percentage = (1 - probability) * 100
-        segment_predictions.append(pred)
-        line = f"Segment {idx + 1}: {'Real' if pred == 1 else 'Fake'} (Confidence: {round(confidence_percentage, 2)}%)"
-        output_lines.append(line)
-    overall_prediction = 1 if sum(segment_predictions) > (len(segment_predictions) / 2) else 0
-    overall_line = f"Overall Prediction: {'Real' if overall_prediction == 1 else 'Fake'}"
-    output_str = overall_line + "\n" + "\n".join(output_lines)
-    return output_str
-
-def gradio_interface(audio):
-    if audio:
-        return process_audio(audio)
-    else:
-        return "Please upload an audio file."
-
-interface = gr.Interface(
-    fn=gradio_interface,
-    inputs=[gr.Audio(type="filepath", label="Upload Audio")],
-    outputs="text",
-    title="SOL2 Audio Deepfake Detection Demo",
-    description="Upload an audio file to check if it's AI-generated. Predictions are done on 10-second segments with majority voting for the overall decision.",
-)
-
-interface.launch(share=True)