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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')
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