When OpenAI open-sourced Whisper, it quietly reset the baseline for what an in-house ASR service looks like. Fine-tuning a strong multilingual foundation model on your own domain is no longer a moonshot — it's a weekend project if you know which levers to pull. This first installment of the series lays out the full path from Whisper checkpoints to a production ASR endpoint tuned to your language, accent, and vocabulary.

The overview breaks the problem into three buckets. Compute: auto fine-tune services from OpenAI and Microsoft versus renting GPUs versus running your own cluster. Data: large-scale weakly labeled domain audio paired with a smaller pool of well-labeled production samples, rather than the classic all-clean-studio-recording approach. Algorithms and deployment: PEFT recipes and existing serving stacks are already good enough that you shouldn't be writing your own from scratch. The rest of the series drills into each piece, but if you want the mental model first, this overview is the right entry point.