Update app.py

app.py

@@ -1,95 +1,97 @@
-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
-import torch.nn.functional as F
-
-
-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,scaler, thresh = joblib.load('logreg_margin_pruning_ALL_with_scaler+threshold.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 = thresh - 5e-3 # small margin error due to feature extractor space differences
-    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)
-        decision_score = classifier.decision_function(features_avg)
-        decision_score_scaled = scaler.transform(decision_score.reshape(-1, 1)).flatten()
-        decision_value = decision_score_scaled[0]
-        pred = 1 if decision_value >= eer_threshold else 0
-        if pred == 1:
-            confidence_percentage = decision_score.item()
-        else:
-            confidence_percentage = 1 - decision_score.item()
-
-        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",
-)
-
-interface.launch(share=True)
-
-#print(process_audio('SSL_scripts/4.wav'))
+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
+import torch.nn.functional as F
+
+
+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,scaler, thresh = joblib.load('logreg_margin_pruning_ALL_with_scaler+threshold.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 = thresh - 5e-3 # small margin error due to feature extractor space differences
+    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)
+        decision_score = classifier.decision_function(features_avg)
+        decision_score_scaled = scaler.transform(decision_score.reshape(-1, 1)).flatten()
+        decision_value = decision_score_scaled[0]
+        pred = 1 if decision_value >= eer_threshold else 0
+        p_real = 1.0 / (1.0 + np.exp(-decision_value))
+        if pred == 1:
+            confidence_percentage = round(p_real * 100, 2)
+
+        else:
+            confidence_percentage = round((1 - p_real) * 100, 2)
+
+        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",
+)
+
+interface.launch(share=True)
+
+#print(process_audio('SSL_scripts/4.wav'))