Demo.py

import librosa
import numpy as np
from tensorflow import keras

# Placeholder values, replace with your actual data and model path
audio_file_path = "path_to_your_audio_file.wav"
model_path = "path_to_your_model/voice_sculptor.h5"

# Load the model
model = keras.models.load_model(model_path)

# Function to extract features from audio file
def extract_features(file_path):
    # Implement your feature extraction logic here
    # ...

# Load and preprocess the audio file
features = extract_features(audio_file_path)A

# Make predictions using the model
predictions = modelA.predict(np.expand_dims(features, axis=0))

# Get the predicted labels
emotion_label = np.argmax(predictions[0])
speaker_label = np.argmax(predictions[1])

# Display the predicted labels
print("Predicted Emotion Label:", emotion_label)
print("Predicted Speaker Label:", speaker_label)