Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import numpy as np
 import librosa
 from tensorflow.keras.models import load_model
-
+import matplotlib.pyplot as plt
 # Constants
 MAX_TIME_STEPS = 109
 SAMPLE_RATE = 16000
@@ -14,6 +14,18 @@ MODEL_PATH = "audio_classifier.h5"  # Replace with the actual path to your saved
 model = load_model(MODEL_PATH, compile=False)
 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
 
+def visualize(mel_spectrogram):
+    median_decibels = np.median(mel_spectrogram)
+    median_human_voice_range = -65
+    diff_decibels = abs(median_decibels - median_human_voice_range)
+    plt.figure(figsize=(12, 6))
+    plt.subplot(1, 1, 1)
+    librosa.display.specshow(mel_spectrogram, sr=SAMPLE_RATE, x_axis='time', y_axis='mel')
+    plt.colorbar(format='%+2.0f dB')
+    plt.title(f'Difference from Median Human Voice Range: {diff_decibels:.2f} dB')
+    plt.savefig("mel_spectrogram.png")  # Save the image
+    plt.close()
+
 def classify_audio(audio):
     # Convert the audio data to NumPy array
     rate, ar = audio
@@ -37,13 +49,16 @@ def classify_audio(audio):
     # Convert probabilities to predicted classes
     y_pred_classes = np.argmax(y_pred, axis=1)
 
-    if(y_pred_classes[0]==1):
-        return f"Prediction: {'Not spoof'}"
+    if y_pred_classes[0] == 1:
+        prediction = "Not Spoof : High chances of original voice"
     else:
-        return f"Prediction: {'Spoof'}"
+        prediction = "Spoof : Possible voice cloning"
+
+    visualize(mel_spectrogram)
+    return prediction,"mel_spectrogram.png"
     
-title="Audios Spoof detection using CNN"
-description="The model was trained on the ASVspoof 2015 dataset with an aim to detect spoof audios through deep learning.To use it please upload an audio file of suitable length."
+title=" Group-2 Audio Spoof detection using CNN"
+description="The model was trained on the ASVspoof 2019 dataset with an aim to detect spoof audios through deep learning.To use it please upload an audio file of suitable length. The Mel spectrogram used for inferencing is also available for the user to understand the classification and compare it with the median Human decibal range."
  
-iface = gr.Interface(classify_audio, inputs=["audio"], outputs=["text"],title=title,description=description)
-iface.launch()
+iface = gr.Interface(classify_audio, inputs=["audio"], outputs=["text","image"],title=title,description=description)
+iface.launch()