01 / DECIDE
Architecture decision
Use it for searchable recordings, captions, meeting notes, and voice interfaces where the original audio remains authoritative.
Treat transcription as a media-processing job with upload validation, language context, segmentation, speaker policy, and correction workflow. Map input, output, state, and side effects as one observable path before deciding which layer owns build transcription around timestamps and uncertainty.
02 / BUILD
Three-step implementation
- 01
Frame the contract
Write down the caller, data classification, success condition, timeout, cancellation, and ownership. Use it for searchable recordings, captions, meeting notes, and voice interfaces where the original audio remains authoritative.
- 02
Build one narrow path
Implement one end-to-end path with request correlation, typed state, and reversible failure handling. Treat transcription as a media-processing job with upload validation, language context, segmentation, speaker policy, and correction workflow.
- 03
Prove the outcome
Turn acceptance into a repeatable fixture, contract test, or browser test. Fixtures measure word error, timestamp drift, named entities, silence, overlap, accents, and corrupted media.
03 / BOUND
Production boundary
Obtain recording consent, minimize retention, and require human review before legal, medical, or disciplinary use.
04 / PROVE
Acceptance evidence
Fixtures measure word error, timestamp drift, named entities, silence, overlap, accents, and corrupted media.
SOURCE / HTTP
Reproducible source probe
curl -fsSI 'https://developers.openai.com/cookbook/examples/speech_transcription_methods' | sed -n '1,5p'