Add application file

app.py

@@ -0,0 +1,115 @@
+import streamlit as st
+import numpy as np
+import torch
+from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2Model
+import torchaudio
+from torchaudio.transforms import Resample
+from tensorflow.keras.models import load_model
+
+# Load models
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/mms-1b")
+wav2vec_model = Wav2Vec2Model.from_pretrained("facebook/mms-1b").to(device)
+
+saved_model_path = "CNN-MODEL"
+try:
+    cnn_model = load_model(saved_model_path)
+except Exception as e:
+    st.error(f"Error loading TensorFlow model: {e}")
+    st.stop()
+
+# Preprocessing Function
+def preprocess_audio(audio_path):
+    try:
+        waveform, sampling_rate = torchaudio.load(audio_path)
+        desired_sampling_rate = 16000
+        if sampling_rate != desired_sampling_rate:
+            resampler = Resample(sampling_rate, desired_sampling_rate)
+            waveform = resampler(waveform)
+        if waveform.shape[0] > 1:
+            waveform = waveform.mean(dim=0, keepdim=True)
+        return waveform, desired_sampling_rate
+    except Exception as e:
+        st.error(f"Error processing audio file: {e}")
+        return None, None
+
+# Feature Extraction
+def extract_features(audio_path, feature_extractor, wav2vec_model, device):
+    waveform, fs = preprocess_audio(audio_path)
+    if waveform is None:
+        return None
+    inputs = feature_extractor(waveform.squeeze().numpy(), sampling_rate=fs, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = wav2vec_model(**inputs)
+        embeddings = outputs.last_hidden_state.cpu().numpy()
+    avg_embeddings = np.mean(embeddings.squeeze(), axis=0)
+    return avg_embeddings
+
+# Prediction
+def predict_with_cnn(audio_path, cnn_model, feature_extractor, wav2vec_model, device):
+    features = extract_features(audio_path, feature_extractor, wav2vec_model, device)
+    if features is None:
+        return None, None, None
+    features = np.expand_dims(features, axis=0)
+    features = np.expand_dims(features, axis=2)
+    predictions = cnn_model.predict(features)
+    predicted_class = np.argmax(predictions, axis=1)
+    class_names = ["bonafide", "spoof"]
+    confidence = predictions[0][predicted_class[0]]  # Extract confidence for predicted class
+    return class_names[predicted_class[0]], predictions[0], confidence
+
+# Streamlit Application
+st.set_page_config(page_title="🎵 Audio Spoof Detection", layout="wide")
+st.title("🎵 Audio Spoof Detection")
+st.markdown(
+    """
+    This application uses advanced machine learning models to detect whether an audio file is **bonafide** (real) or **spoofed** (fake).
+    Upload a `.wav` file to get started!
+    """
+)
+
+# File Upload
+uploaded_file = st.file_uploader(
+    "Upload your audio file (WAV format only):", type=["wav"]
+)
+
+if uploaded_file:
+    # Save uploaded file to a temporary path
+    temp_file_path = "temp_audio.wav"
+    with open(temp_file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    # Display Audio Player
+    st.audio(temp_file_path, format="audio/wav")
+
+    # Processing Audio
+    st.write("🎧 **Processing the audio...**")
+    predicted_class, probabilities, confidence = predict_with_cnn(
+        temp_file_path, cnn_model, feature_extractor, wav2vec_model, device
+    )
+    
+    # Display Results
+    if predicted_class:
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.markdown(
+                f"""
+                ## 🎉 **Prediction: `{predicted_class.upper()}`**
+                """
+            )
+            st.markdown(f"### **Confidence**: `{confidence:.2f}`")
+
+        with col2:
+            st.write("### Class Probabilities")
+            st.bar_chart(probabilities)
+
+        # Display Detailed Probabilities
+        st.markdown("### Class Details")
+        st.write(f"**Bonafide Probability**: `{probabilities[0]:.2f}`")
+        st.write(f"**Spoof Probability**: `{probabilities[1]:.2f}`")
+
+    else:
+        st.error("Failed to process the audio file. Please try again.")
+else:
+    st.info("Please upload a `.wav` audio file to analyze.")