Upload 5 files

.gitattributes

@@ -33,3 +33,5 @@ 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
+audio_files/recorded_audio.wav filter=lfs diff=lfs merge=lfs -text
+model/model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,133 @@
+
+
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import librosa
+import os
+from PIL import Image
+from io import BytesIO
+import tensorflow as tf
+from st_audiorec import st_audiorec
+import altair
+import keras
+import librosa.display
+import matplotlib.pyplot as plt
+from keras_preprocessing.image import load_img, img_to_array
+
+os.environ["KERAS_BACKEND"] = "tensorflow"
+
+st.set_page_config(page_title="Deepfake Audio")
+class_names = ['real', 'fake']
+
+
+def file_save(file_sound):
+    with open(os.path.join('audio_files/', file_sound.name), 'wb') as f:
+        f.write(file_sound.getbuffer())
+
+    return file_sound.name
+
+
+def create_spec(sound):
+    audio_file = os.path.join('audio_files/', sound)
+
+    fig = plt.figure()
+    ax = fig.add_subplot(1, 1, 1)
+    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
+    y, sr = librosa.load(audio_file)
+    mel = librosa.feature.melspectrogram(y=y, sr=sr)
+    log_ms = librosa.power_to_db(mel, ref=np.max)
+    librosa.display.specshow(log_ms, sr=sr)
+    plt.savefig('mel_spectrogram.png')
+    image_data = load_img('mel_spectrogram.png', target_size=(224, 224))
+    st.image(image_data)
+
+    return image_data
+
+
+def pred(image_data, model):
+    img_array = np.array(image_data)
+    img_array1 = img_array / 255
+    img_batch = np.expand_dims(img_array1, axis=0)
+
+    prediction = model.predict(img_batch)
+    class_label = np.argmax(prediction)
+
+    return class_label, prediction
+
+
+def file_upload_page():
+    st.write("## File Upload Page")
+    uploaded_file = st.file_uploader('Upload a .wav or .mp3 file', type=['wav', 'mp3'])
+    if uploaded_file is not None:
+        st.write('### Play audio')
+        audio_bytes = uploaded_file.read()
+        st.audio(audio_bytes, format='audio/wav')
+
+        st.write('### Spectrogram Image:')
+        file_save(uploaded_file)
+        sound = uploaded_file.name
+        with st.spinner('Fetching Results...'):
+            spec = create_spec(sound)
+            model = tf.keras.models.load_model('model/model.keras')
+        st.write('### Classification results:')
+        class_label, prediction = pred(spec, model)
+        st.write("#### The uploaded audio file is " + class_names[class_label])
+
+
+def record_audio_page():
+
+    st.write("### Record Your Voice")
+    st.write("- ** After that it will automatically process and gives results that audio file is real or fake(AI generated)")
+    wav_audio_data = st_audiorec()
+
+    if wav_audio_data is not None:
+        st.audio(wav_audio_data, format='audio/wav')
+        st.write("### Spectrogram Image:")
+        # Save the recorded audio as a file
+        with open('audio_files/recorded_audio.wav', 'wb') as f:
+            f.write(wav_audio_data)
+        sound = 'recorded_audio.wav'
+        with st.spinner('Fetching Results...'):
+            spec = create_spec(sound)
+            model = tf.keras.models.load_model('model/model.keras')
+        st.write('### Classification results:')
+        class_label, prediction = pred(spec, model)
+        st.write("#### The recorded audio is " + class_names[class_label])
+
+
+def main():
+    # Default page
+
+
+    # Sidebar to switch between pages
+    page_options = ['Information', 'Upload Audio File', 'Record Audio']
+    selected_page = st.sidebar.selectbox('Select Page', page_options)
+
+    # Show corresponding page based on selection
+    if selected_page == 'Information':
+        show_information_page()
+    elif selected_page == 'Upload Audio File':
+        file_upload_page()
+    elif selected_page == 'Record Audio':
+        record_audio_page()
+
+
+def show_information_page():
+    st.write("## Deepfake Audio Classification")
+    st.write("This web app allows you to classify audio files as real or fake.")
+    st.write("Please select an option from the dropdown menu to proceed.")
+
+    st.write("## Information Page")
+    st.write("This page provides information about the Deepfake Audio Classification web app.")
+
+    st.write("## Audio Features")
+    st.write("- **Spectrogram:** A visual representation of the audio frequency content.")
+    st.write("- **Classification results:** The prediction of whether the audio is real or fake.")
+    st.write("- **Model:** Deep learning model trained to classify audio files.")
+
+
+
+if __name__ == "__main__":
+    main()

audio_files/recorded_audio.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39716a7a42fb1f0a709098969ae66fcd53072375efe0e2d5ca7032d0c0e8ece6
+size 2392108

model/model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8f62fa0d7f9f1285a58597bb48677088cd2097cc9214a8e470650dddbb49ce6
+size 13220801

requirements.txt

@@ -0,0 +1,11 @@
+streamlit==1.10.0
+opencv-python
+
+librosa
+tensorflow
+matplotlib
+keras
+Keras-Preprocessing
+lime
+scikit-image
+numpy