Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import librosa
+from tensorflow.keras.models import load_model
+
+# Constants
+MAX_TIME_STEPS = 109
+SAMPLE_RATE = 16000
+DURATION = 5
+N_MELS = 128
+MODEL_PATH = "audio_classifier.h5"  # Replace with the actual path to your saved model
+
+# Load the pre-trained model
+model = load_model(MODEL_PATH, compile=False)
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+def classify_audio(audio):
+    # Convert the audio data to NumPy array
+    rate, ar = audio
+    arone = ar.astype(np.float32)
+    mel_spectrogram = librosa.feature.melspectrogram(y=arone, sr=SAMPLE_RATE, n_mels=N_MELS)
+    mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)
+
+    # Ensure all spectrograms have the same width (time steps)
+    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]
+
+    # Reshape for the model
+    X_test = np.expand_dims(mel_spectrogram, axis=-1)
+    X_test = np.expand_dims(X_test, axis=0)
+
+    # Predict using the loaded model
+    y_pred = model.predict(X_test)
+
+    # 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'}"
+    else:
+        return f"Prediction: {'Spoof'}"
+    
+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."
+
+iface = gr.Interface(classify_audio, inputs=["audio"], outputs=["text"],title=title,description=description)
+iface.launch()