For this project, I designed and fine-tuned a state-of-the-art text-to-speech (TTS) system using StyleTTS2, a cutting-edge diffusion-based speech synthesis model. The goal was to create a highly natural and expressive synthetic voice, trained on custom voice data provided by the client.

My responsibilities included:

Data Preparation: Curated and pre-processed over an hour of high-quality, phonetically diverse audio recordings to maximize coverage of tones, words, and expressions.

Model Fine-tuning: Customized StyleTTS2 on the client’s voice dataset to capture unique vocal characteristics, intonation, and natural prosody.

Evaluation & Iteration: Conducted rigorous A/B testing on generated samples to ensure clarity, realism, and consistency across various sentence structures and emotions.

Optimization for Production: Optimized inference speed and model performance, making it scalable for frequent content generation.

Outcome: The client successfully used the fine-tuned TTS model to produce high-quality, faceless video content for platforms like Instagram, X (Twitter), YouTube, and more. This allowed them to scale content creation efficiently while maintaining a unique and consistent brand voice.

This project showcases expertise in AI/ML (deep learning, speech synthesis, diffusion models), data engineering, and practical deployment of generative AI models for real-world content creation.