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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+temp_audio_file.wav filter=lfs diff=lfs merge=lfs -text

model_breath_logspec_mfcc_cnn.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6fea718318cecbaaded6f0061747c58e61006ec133550626e3466478f5203c97
+size 137115984

requirements.txt

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+numpy==1.26.0
+librosa==0.10.1
+tensorflow==2.19.0
+streamlit==1.43.2
+

streamlit_App.py

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+import numpy as np
+import librosa
+import tensorflow as tf
+import streamlit as st
+
+window_length = 0.02  # 20ms window length
+hop_length = 0.0025  # 2.5ms hop length
+sample_rate = 22050  # Standard audio sample rate
+n_mels = 128  # Number of mel filter banks
+threshold_zcr = 0.1  # Adjust this threshold to detect breath based on ZCR
+threshold_rmse = 0.1  # Adjust this threshold to detect breath based on RMSE
+
+def extract_breath_features(y, sr):
+    frame_length = int(window_length * sr)
+    hop_length_samples = int(hop_length * sr)
+    
+    zcr = librosa.feature.zero_crossing_rate(y=y, frame_length=frame_length, hop_length=hop_length_samples)
+    rmse = librosa.feature.rms(y=y, frame_length=frame_length, hop_length=hop_length_samples)
+    
+    zcr = zcr.T.flatten()
+    rmse = rmse.T.flatten()
+    
+    # Calculate breath events
+    breaths = (zcr > threshold_zcr) & (rmse > threshold_rmse)
+    
+    # Create a breath feature: 1 if breath is present, else 0
+    breath_feature = np.where(breaths, 1, 0)
+    
+    return breath_feature
+
+def extract_features(file_path, n_mels=128, n_cqt=84, max_len=500, n_mfcc=13):
+    try:
+        y, sr = librosa.load(file_path, sr=None)
+        
+        # Compute MFCC
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=n_mfcc)
+        mfcc = librosa.util.fix_length(mfcc, size=max_len, axis=1)  # Fix length
+        
+        # Compute log-mel spectrogram
+        logspec = librosa.amplitude_to_db(librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels))
+        logspec = librosa.util.fix_length(logspec, size=max_len, axis=1)  # Fix length
+        
+        # Extract breath features
+        breath_feature = extract_breath_features(y, sr)
+        breath_feature = librosa.util.fix_length(breath_feature, size=max_len)  # Fix length
+        
+        # Stack features vertically
+        return np.vstack((mfcc,logspec, breath_feature))
+    except Exception as e:
+        print(f"Error loading {file_path}: {e}")
+        return None
+
+# Function to prepare the features for prediction
+def prepare_single_data(features, max_len=500):
+    features = librosa.util.fix_length(features, size=max_len, axis=1)
+    features = features[np.newaxis, ..., np.newaxis]  # Add batch and channel dimensions
+    return features
+
+# Load the saved TensorFlow Lite model
+interpreter = tf.lite.Interpreter(model_path=r"model_breath_logspec_mfcc_cnn.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Function to predict audio class
+def predict_audio(file_path):
+    features = extract_features(file_path)
+    if features is not None:
+        prepared_features = prepare_single_data(features)
+        # Ensure the prepared features are of type FLOAT32
+        prepared_features = prepared_features.astype(np.float32)  # Convert to FLOAT32
+        # Set the tensor to the prepared input data
+        interpreter.set_tensor(input_details[0]['index'], prepared_features)
+        interpreter.invoke()
+        # Get the prediction result
+        prediction = interpreter.get_tensor(output_details[0]['index'])
+        predicted_class = np.argmax(prediction, axis=1)
+        predicted_prob = prediction[0]  # Get the probabilities for EER calculation
+        return predicted_class[0], predicted_prob  # Return class index and probabilities
+    else:
+        return None, None
+
+# Streamlit app
+st.title('Audio Classification: Real vs Fake')
+st.write('Upload an audio file to classify it as real or fake.')
+
+# File uploader
+uploaded_file = st.file_uploader('Choose an audio file', type=['wav', 'mp3'])
+
+if uploaded_file is not None:
+    # Save the uploaded file temporarily
+    with open('temp_audio_file.wav', 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+
+
+    # Predict using the loaded model
+    prediction,probablity = predict_audio('temp_audio_file.wav')
+    st.write(f'Predicted class is {prediction} \n')
+    st.write(f'Probability of being real: {probablity[0]*100:.2f}% \n')
+    st.write(f'Probability of being fake: {probablity[1]*100:.2f}% \n')
+    
+
+    
+    
+    
+
+ 

temp_audio_file.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:160a44a876905d90490a048202b70ca8e5685375fa14ed69280948157486e475
+size 2044844