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app_voice.py

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+# -*- coding: utf-8 -*-
+"""app_voice.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1op-dtpDLHXAJm53Q-2S04nNsQGjcz18G
+"""
+
+import os
+import numpy as np
+import librosa
+import gradio as gr
+from tensorflow.keras.models import load_model
+from sklearn.preprocessing import LabelEncoder
+import warnings
+warnings.filterwarnings("ignore")
+
+# Load trained model
+model = load_model("voice_verifier_model.h5")
+
+# Load label encoder
+encoder = LabelEncoder()
+encoder.classes_ = np.array(['Fake', 'Real'])  # Adjust if your label order is different
+
+# Feature extraction
+def extract_features(file_path):
+    try:
+        audio, sample_rate = librosa.load(file_path, duration=3, offset=0.5)
+        mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)
+        return np.mean(mfccs.T, axis=0)
+    except Exception as e:
+        print("Audio processing error:", e)
+        return None
+
+# Prediction function
+def predict_audio(file):
+    features = extract_features(file)
+    if features is None:
+        return "⚠️ **Oops! Couldn't understand the audio. Try again with a clear `.wav` file.**"
+
+    features = features.reshape(1, -1)
+    probs = model.predict(features)[0]
+    index = np.argmax(probs)
+    label = encoder.inverse_transform([index])[0]
+    confidence = round(probs[index] * 100, 2)
+
+    if label.lower() == "real":
+        emoji = "🧠🗣️"
+        msg = f"{emoji} **Real Human Voice Detected!**\n🟢 Confidence: **{confidence}%**"
+        advice = "✅ No robots here. It's a real person!"
+    else:
+        emoji = "🤖🎙️"
+        msg = f"{emoji} **AI-Generated Voice Detected!**\n🔴 Confidence: **{confidence}%**"
+        advice = "⚠️ Synthetic voice detected. Be cautious!"
+
+    return f"{msg}\n\n{advice}"
+
+# App description
+description = """
+🎙️ Welcome to **Voice Verifier 3000**
+🔍 Detect whether a voice is **REAL** or **AI-generated** using a deep learning model trained on human vs synthetic audio.
+
+---
+
+### 🤖 Why Use This?
+- 🛡️ Catch deepfake voices in seconds
+- 🎙️ Validate voiceovers, interviews, or online calls
+- 🔍 Useful for researchers, content moderators, or just curious minds
+
+---
+
+### 📂 How to Use:
+1. Upload a `.wav` file (3–5 seconds)
+2. Click **Submit**
+3. Instantly see the voice verdict with confidence level!
+
+---
+
+🔥 Built with ❤️ using TensorFlow + Librosa + Gradio
+"""
+
+# Gradio UI
+gr.Interface(
+    fn=predict_audio,
+    inputs=gr.Audio(type="filepath", label="📂 Upload your voice (.wav only)"),
+    outputs="markdown",
+    title="🧠 Voice Verifier 3000: Human vs AI Voice Detector",
+    description=description,
+    theme="default"
+).launch()

requirements.txt

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+librosa
+numpy
+scikit-learn
+gradio
+tensorflow

voice_verifier_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65963c05efc171691f7c869ff1a4949a1ad586d05f1e7a2bdec26702915d8f9b
+size 197064