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Voice cloning is an advanced AI-driven technology that replicates a person’s voice with high accuracy. It leverages deep learning architectures, spectrogram analysis, and neural vocoders to generate natural-sounding speech. Applications include personalized virtual assistants, entertainment, dubbing, accessibility for disabled users, and interactive communication systems. However, challenges arise in terms of ethical concerns, prevention of misuse, and maintaining emotional prosody. This project proposes a deep learning-based framework that integrates Tacotron2, WaveNet, and VITS models for high-fidelity speech synthesis. Speech datasets are preprocessed using Mel-frequency cepstral coefficients (MFCCs) and spectrograms for effective feature extraction. The system is integrated into a user-friendly interface using Streamlit/Flask, enabling real-time inference and interactive testing. The proposed framework achieves high-quality, human-like voice generation while addressing misuse risks through safeguards like watermarking and misuse detection. The model is lightweight, scalable, and adaptable to multilingual and emotion-aware synthesis, making it suitable for real-world deployment in healthcare, accessibility, and entertainment domains.The system ensures effective representation of speech signals, facilitating the generation of natural-sounding voice clones. The system is designed to be user-friendly, integrating a web-based interface built with Streamlit and Flask, allowing users to interact with the system in real time.