🔥 Fuzzu Voice AI - Real-Time Gender & Age Detector using Python | Viral Tech Demo

   Demo :

Click Video 👇👇👇

Features:

• Voice recording with sounddevice

• Feature extraction with librosa

• ML prediction using dummy models

• Tkinter GUI interface

Code :

import os

import librosa

import numpy as np

import sounddevice as sd

import scipy.io.wavfile as wav

from sklearn.preprocessing import StandardScaler

from tkinter import *

from tkinter import messagebox

# Constants

DURATION = 5  # seconds

SAMPLE_RATE = 22050

FILENAME = "record.wav"

# Dummy model simulation (mocked for this example)

def load_models():

    class DummyModel:

        def predict(self, X):

            return ["Male"] if np.mean(X) > 0 else ["Female"]

    class DummyAgeModel:

        def predict(self, X):

            avg = np.mean(X)

            if avg < -5:

                return ["Child"]

            elif avg < 0:

                return ["Teen"]

            elif avg < 5:

                return ["Adult"]

            else:

                return ["Senior"]

    return DummyModel(), DummyAgeModel()

gender_model, age_model = load_models()

# Record function

def record_audio():

    print("Recording started...")

    audio = sd.rec(int(DURATION * SAMPLE_RATE), samplerate=SAMPLE_RATE, channels=1)

    sd.wait()

    # Normalize and convert to int16 before saving

    audio = audio.flatten()

    audio = np.int16(audio / np.max(np.abs(audio)) * 32767)

    wav.write(FILENAME, SAMPLE_RATE, audio)

    print("Recording saved as record.wav")

# Extract MFCCs

def extract_features(file_path):

    y, sr = librosa.load(file_path, sr=SAMPLE_RATE)

    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)

    scaler = StandardScaler()

    mfcc_scaled = scaler.fit_transform(mfcc.T)

    mfcc_mean = np.mean(mfcc_scaled, axis=0)

    return mfcc_mean.reshape(1, -1)

# Predict using dummy models

def predict():

    if not os.path.exists(FILENAME):

        messagebox.showwarning("Warning", "Please record audio first!")

        return

    features = extract_features(FILENAME)

    gender = gender_model.predict(features)[0]

    age = age_model.predict(features)[0]

    result = f"Predicted Gender: {gender}\nPredicted Age Group: {age}"

    messagebox.showinfo("Prediction", result)

# GUI setup

root = Tk()

root.title("Fuzzu Voice AI - Gender & Age Detector")

root.geometry("400x300")

root.config(bg="#1e1e2f")

title = Label(root, text="🎤 Voice Gender & Age Detector", fg="white", bg="#1e1e2f", font=("Helvetica", 16))

title.pack(pady=20)

record_btn = Button(root, text="🎙️ Record", font=("Helvetica", 14), command=record_audio, bg="#4caf50", fg="white", padx=10, pady=5)

record_btn.pack(pady=10)

predict_btn = Button(root, text="🤖 Predict", font=("Helvetica", 14), command=predict, bg="#2196f3", fg="white", padx=10, pady=5)

predict_btn.pack(pady=10)

root.mainloop()