Bajibabu Bollepalli is an Applied Machine Learning Scientist with 12 years of experience specializing in speech technology, particularly generative modelling, text-to-speech and voice conversion. He holds a Doctor of Science in Speech and Language Technology from Aalto University where his PhD focused on artificial speech generation and vocoder/style adaptation techniques. Bajibabu has applied his research in industry roles at Amazon and Verisk and now works on production ML at Sanas, bridging academic advances with deployable solutions. His open-source contributions include speaker adaptation work for the well-known Merlin TTS project, highlighting a practical focus on making models adaptable to new voices. Technically fluent across PyTorch, TensorFlow, probabilistic models and classical signal processing, he brings both deep learning and systems-level expertise to speech pipelines. Colleagues describe him as someone who pairs rigorous research instincts with hands-on engineering to push the boundaries of natural-sounding synthetic speech.
11 years of coding experience
2 years of employment as a software developer
Doctor of Science (Technology), Speech and Language Technology, Pass, Doctor of Science (Technology), Speech and Language Technology, Pass at Aalto University
Full Stack Web Development Certification, Computer Software Engineering, Full Stack Web Development Certification, Computer Software Engineering at Free Code Camp
Bachelor of Technology (B.Tech.) + Master of Science (M.S), Electronics and Communications Engineering, Bachelor of Technology (B.Tech.) + Master of Science (M.S), Electronics and Communications Engineering at International Institute of Information Technology
This is now the official location of the Merlin project.
Role in this project:
ML Engineer
Contributions:5 commits, 7 PRs, 58 comments in 1 year
Contributions summary:Bajibabu primarily contributed to speaker adaptation within the Merlin project, focusing on adapting existing models to new speakers. Their work involved modifying training scripts to load and fine-tune pre-trained models, specifically incorporating speaker adaptation techniques. They also addressed minor bug fixes, path modifications for configuration files, and added configuration files. These changes suggest a focus on enhancing the model's adaptability and overall functionality.
Contributions:5 commits, 3 pushes, 1 branch in 11 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial
Bajibabu Bollepalli - Applied Machine Learning Scientist at Sanas