Voice AI Lab
A broad practical curriculum for modern speech AI in both directions: speech → text and text → speech.
The recognition track covers Whisper-family models, open alternatives, cloud APIs, long-form transcription, translation, timestamps, forced alignment, diarization, VAD, and streaming. The generation track covers open TTS models, voice cloning, expressive and multi-speaker synthesis, cloud APIs, low-latency streaming, and realtime speech-to-speech systems.
The lab emphasizes comparative engineering: what model to reach for, what hardware and licensing constraints matter, how dependencies conflict, and what latency/quality trade-offs appear in practice. Heavier model families run in isolated environments so experiments remain reproducible rather than turning dependency management into the experiment.