Project Title:Voice Cloning System Using Machine Learning

Objective:

To build a machine learning-based system that can generate synthetic speech mimicking a specific person's voice from just a few audio samples.

Summary:

This project involves creating a voice cloning system that can replicate a person's voice by analyzing and learning from a small amount of recorded speech. Using deep learning models, the system captures the unique vocal features—like pitch, tone, and speaking style—and then uses them to synthesize new speech that sounds like the original speaker.

The project typically involves three key components:

Speaker Encoding: Identifies unique voice features from input samples.

Text-to-Speech (TTS) Model: Converts written text to speech in the cloned voice.

Vocoder: Turns the generated spectrogram into realistic audio (e.g., using WaveNet or HiFi-GAN).

Pre-trained models like Tacotron 2, SV2TTS, or FastSpeech are commonly used in the implementation.

Key Steps:

Collect Voice Samples – Record or use sample clips of the target speaker.

Preprocess Audio – Clean, trim, and convert to spectrograms.

Train/Use Models – Use voice encoder, TTS, and vocoder models.

Generate Cloned Speech – Input any text and get output in the target voice.

Technologies Used:

Python

PyTorch / TensorFlow

Librosa (audio processing)

Pretrained models: Tacotron 2, SV2TTS, WaveNet, or HiFi-GAN

Applications:

Personalized voice assistants

Audiobook narration

Voice dubbing in media

Accessibility tools (for people who lose their voice)

Expected Outcomes:

A system that takes a few seconds of speech and generates realistic cloned voice audio

A user interface (optional) for inputting text and playing the generated audio

Evaluation of voice similarity and naturalness