Upload streamlit_app.py

streamlit_app.py

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+# streamlit_app.py
+
+import streamlit as st
+import os
+import librosa
+import librosa.display
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import tensorflow as tf
+from tensorflow.keras.utils import to_categorical
+from sklearn.metrics import confusion_matrix, roc_curve, auc, precision_recall_curve, average_precision_score, calibration_curve, ConfusionMatrixDisplay
+from keras.models import load_model
+
+SAMPLE_RATE = 16000
+DURATION = 5
+N_MELS = 128
+MAX_TIME_STEPS = 109
+NUM_CLASSES = 2
+
+# Streamlit App
+st.title("Audio Spoofing Detection App")
+
+st.sidebar.header("Model Options")
+task = st.sidebar.selectbox("Select Task", ["Train Model", "Evaluate Model", "Visualize Spectrogram"])
+
+if task == "Train Model":
+    st.header("Train a New Model")
+    
+    uploaded_files = st.file_uploader("Upload FLAC Training Files", accept_multiple_files=True, type='flac')
+    label_file = st.file_uploader("Upload Labels File (txt)", type="txt")
+
+    if uploaded_files and label_file:
+        # Parse the label file
+        labels = {}
+        for line in label_file.getvalue().decode("utf-8").splitlines():
+            parts = line.strip().split()
+            file_name = parts[1]
+            label = 1 if parts[-1] == "bonafide" else 0
+            labels[file_name] = label
+
+        X, y = [], []
+        for file in uploaded_files:
+            file_name = file.name.split(".")[0]
+            label = labels[file_name]
+
+            # Load audio file
+            audio, _ = librosa.load(file, sr=SAMPLE_RATE, duration=DURATION)
+
+            # Extract Mel spectrogram
+            mel_spectrogram = librosa.feature.melspectrogram(y=audio, sr=SAMPLE_RATE, n_mels=N_MELS)
+            mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)
+
+            # Padding
+            if mel_spectrogram.shape[1] < MAX_TIME_STEPS:
+                mel_spectrogram = np.pad(mel_spectrogram, ((0, 0), (0, MAX_TIME_STEPS - mel_spectrogram.shape[1])), mode='constant')
+            else:
+                mel_spectrogram = mel_spectrogram[:, :MAX_TIME_STEPS]
+
+            X.append(mel_spectrogram)
+            y.append(label)
+
+        X = np.array(X)
+        y = np.array(y)
+
+        y_encoded = to_categorical(y, NUM_CLASSES)
+
+        # Split into train and validation sets
+        split_index = int(0.8 * len(X))
+        X_train, X_val = X[:split_index], X[split_index:]
+        y_train, y_val = y_encoded[:split_index], y_encoded[split_index:]
+
+        input_shape = (N_MELS, X_train.shape[2], 1)
+        
+        # Define CNN model
+        model_input = tf.keras.Input(shape=input_shape)
+        x = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu')(model_input)
+        x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
+        x = tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu')(x)
+        x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
+        x = tf.keras.layers.Flatten()(x)
+        x = tf.keras.layers.Dense(128, activation='relu')(x)
+        x = tf.keras.layers.Dropout(0.5)(x)
+        model_output = tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')(x)
+
+        model = tf.keras.Model(inputs=model_input, outputs=model_output)
+        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+        
+        # Train the model
+        if st.button("Start Training"):
+            st.write("Training in progress...")
+            model.fit(X_train, y_train, batch_size=32, epochs=10, validation_data=(X_val, y_val))
+            model.save("audio_classifier.h5")
+            st.success("Training Complete. Model Saved!")
+
+if task == "Evaluate Model":
+    st.header("Evaluate a Trained Model")
+
+    model_file = st.file_uploader("Upload Model (h5)", type='h5')
+    test_files = st.file_uploader("Upload Test FLAC Files", accept_multiple_files=True, type='flac')
+    protocol_file = st.file_uploader("Upload Protocol File (txt)", type='txt')
+
+    if model_file and test_files and protocol_file:
+        # Load Model
+        model = load_model(model_file)
+
+        # Prepare test data
+        X_test = []
+        for file in test_files:
+            audio, _ = librosa.load(file, sr=SAMPLE_RATE, duration=DURATION)
+            mel_spectrogram = librosa.feature.melspectrogram(y=audio, sr=SAMPLE_RATE, n_mels=N_MELS)
+            mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)
+            if mel_spectrogram.shape[1] < MAX_TIME_STEPS:
+                mel_spectrogram = np.pad(mel_spectrogram, ((0, 0), (0, MAX_TIME_STEPS - mel_spectrogram.shape[1])), mode='constant')
+            else:
+                mel_spectrogram = mel_spectrogram[:, :MAX_TIME_STEPS]
+            X_test.append(mel_spectrogram)
+
+        X_test = np.array(X_test)
+
+        y_pred = model.predict(X_test)
+        y_pred_classes = np.argmax(y_pred, axis=1)
+
+        # Parse the true labels
+        true_labels = {}
+        for line in protocol_file.getvalue().decode("utf-8").splitlines():
+            parts = line.strip().split()
+            if len(parts) > 1:
+                file_name = parts[0]
+                label = parts[-1]
+                true_labels[file_name] = 1 if label == "bonafide" else 0
+
+        y_true = np.array([label for label in true_labels.values()])
+
+        # Confusion Matrix
+        cm = confusion_matrix(y_true, y_pred_classes)
+        ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Spoof", "Bonafide"]).plot(cmap=plt.cm.Blues)
+        st.pyplot(plt)
+
+        # ROC Curve
+        y_pred_prob = y_pred[:, 1]
+        fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
+        roc_auc = auc(fpr, tpr)
+        plt.figure()
+        plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')
+        plt.legend(loc="lower right")
+        st.pyplot(plt)
+
+        # Precision-Recall Curve
+        precision, recall, _ = precision_recall_curve(y_true, y_pred_prob)
+        avg_precision = average_precision_score(y_true, y_pred_prob)
+        plt.figure()
+        plt.plot(recall, precision, color='darkorange', lw=2, label=f'Avg. Precision = {avg_precision:.2f}')
+        st.pyplot(plt)
+
+if task == "Visualize Spectrogram":
+    st.header("Visualize Mel Spectrogram")
+
+    test_files = st.file_uploader("Upload Test FLAC Files", accept_multiple_files=True, type='flac')
+
+    if test_files:
+        for file in test_files:
+            audio, _ = librosa.load(file, sr=SAMPLE_RATE, duration=DURATION)
+            mel_spectrogram = librosa.feature.melspectrogram(y=audio, sr=SAMPLE_RATE, n_mels=N_MELS)
+            mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)
+
+            plt.figure(figsize=(10, 6))
+            librosa.display.specshow(mel_spectrogram, x_axis='time', y_axis='mel', sr=SAMPLE_RATE)
+            plt.colorbar(format='%+2.0f dB')
+            plt.title(f'Mel Spectrogram - {file.name}')
+            st.pyplot(plt)