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| 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 | |
| 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 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 | |
| arone = ar.astype(np.float32) | |
| if arone.ndim > 1: | |
| arone = arone.flatten() | |
| 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: | |
| prediction = "Not Spoof : High chances of original voice" | |
| else: | |
| prediction = "Spoof : Possible voice cloning" | |
| median_decibels = np.median(mel_spectrogram) | |
| median_human_voice_range = -65 | |
| diff_decibels = abs(median_decibels - median_human_voice_range) | |
| if diff_decibels<3 and y_pred_classes[0] == 0 : | |
| prediction = "Not Spoof : High chances of original voice" | |
| visualize(mel_spectrogram) | |
| return prediction,"mel_spectrogram.png" | |
| 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 decibel range." | |
| iface = gr.Interface(classify_audio, inputs=["audio"], outputs=["text","image"],title=title,description=description) | |
| iface.launch() |