Audio AI for Beginners turns theory into working systems. If you want to build accurate speech recognition, create expressive TTS, or clone voices responsibly - and ship those systems to users - this book is your fast, factual roadmap.
Start with signal fundamentals (sampling, spectrograms, MFCCs), then move straight into practical workflows: run Whisper locally, fine-tune a TTS pipeline with Coqui, extract speaker embeddings, and prototype zero-shot voice cloning. Each chapter includes step-by-step projects, code links, and production checklists so you can move from prototype to deployment without guesswork.
What makes this different:
- Hands-on projects - 6 reproducible builds (ASR, TTS, cloning, voice-preserving translation, a low-latency assistant and a privacy-first on-device demo).
- Production playbooks - latency budgets, model optimization, and deployment recipes for on-device and cloud.
- Ethics & governance - consent templates, watermarking strategies, and risk-checks you can implement today.
- Resources included - GitHub code, dataset pointers, and quick-start scripts for local experiments.
Whether you're an engineer, product lead, or curious practitioner, this book gives you the vocabulary, code, and real-world practices to build trustworthy audio AI. Skip the hype - build reliable, responsible voice systems that work in the wild.
