Define what correct means
A verbatim transcript and an edited transcript serve different needs. Before testing, decide whether filler removal, punctuation and correction resolution count as improvements, tolerated transformations or errors for the intended workflow.
Create two references when necessary: a literal record aligned to the audio and an approved readable version. Score critical facts against both so a polished sentence cannot hide that the model selected the wrong date or removed meaningful hesitation.
Use a layered scorecard
Measure word error rate, entity accuracy, speaker attribution, timestamp error, language detection and time to final stable text. For live use, track how often interim words change because unstable captions can be unusable even when the final sentence is accurate.
Evaluate representative noise, overlap, accents, jargon and code-switching. Keep a hidden set of fresh recordings so prompt tuning or vocabulary lists do not overfit the examples used during development.
Audit transformations
Flag every deletion and rewrite involving negation, uncertainty, numbers, names or a speaker's correction. Give reviewers playback around the transformed segment, and preserve raw audio and model settings long enough to resolve disputes under an explicit retention policy.
AINewsInu's homepage and Research & Data hub connect model claims to evaluation methods. Publish the endpoint, mode, languages, hardware and reference policy so readers can understand why a smart-transcription score may differ from conventional WER.
Sources & further reading
Social-media activity is treated as a signal of attention, not proof. Product claims are attributed to the linked publisher or announcement.