Accuracy is the first question everyone asks about AI transcription, and it is also the hardest to answer honestly. The truth is that there is no single accuracy figure that holds true for every recording. A clear studio interview and a noisy phone call captured on a busy street are completely different problems, and the same system can handle one almost flawlessly while struggling with the other. So instead of chasing a headline percentage, the more useful question is how you measure accuracy on the audio you actually work with.
The most common metric is word error rate, often shortened to WER. It counts the words the system got wrong by adding up substitutions, insertions, and deletions, then dividing by the number of words that were actually spoken. A lower number is better, and zero would mean a perfect match against a human reference transcript. WER is useful because it is simple and widely understood, but it treats every word as equally important. Missing a filler word counts the same as missing a person's name or a critical figure, even though one of those mistakes matters far more for your use case.
That is why raw WER should never be read in isolation. Real recordings are full of variables that shift the result: background noise, overlapping speakers, accents, technical vocabulary, audio compression, and even the microphone. A benchmark run on pristine audio will always look better than the messy reality of conference calls, field recordings, and user-submitted uploads. The honest approach is to test on samples that resemble your real material, not on a curated demo clip that was chosen because it sounds good.
RealtimeVoiceKIT is built so you can do exactly that kind of evaluation. You can upload your own audio or video, or point the service at an audio URL, and get back a full transcript without configuring a pipeline first. Each transcript includes per-word confidence scores, so you can see where the system was sure and where it hesitated. Reviewing the low-confidence words is often faster than rereading the entire transcript, because it points your attention straight to the spots most likely to need a human correction.
Speaker labels are another piece of the accuracy picture that pure WER ignores. A transcript can have every word right and still be hard to use if it does not tell you who said what. RealtimeVoiceKIT applies speaker labels so multi-person recordings, like interviews and meetings, stay readable and attributable. When you benchmark, check not just whether the words are correct but whether turns are split between speakers cleanly, since that affects how usable the output really is.
Timestamps and subtitles are where accuracy becomes practical. Word-level timing lets you jump straight to any moment in the source audio to verify a passage, and exported SRT and VTT subtitles must line up with the video to be worth anything. When you evaluate, scrub through a few captioned sections and confirm the text appears in sync. A transcript that reads perfectly but drifts out of time will still frustrate viewers, so timing is part of any honest accuracy assessment.
Translation adds a second layer to evaluate. When you translate a transcript into another language, errors can come from two places: the original transcription and the translation step itself. The practical method is to confirm the source transcript is solid first, then review the translated version for meaning and tone rather than word-for-word equivalence. Have someone fluent in the target language skim a few passages, since a small transcription slip can be amplified once it is carried into another language.
The most reliable way to know how accurate transcription will be for you is to run your own benchmark on representative audio and read the confidence scores honestly. Start with a handful of recordings that reflect your hardest conditions, transcribe them, and spot-check the results against what you can hear. If you build on the rtvk_ developer API, you can automate that loop and track accuracy over time across plans and projects. Vendors can quote impressive numbers, but the benchmark that matters is the one run on your own files, and RealtimeVoiceKIT gives you the tools to run it.
RealtimeVoiceKit Team writes about audio, AI, and the workflows that turn recordings into reach for the RealtimeVoiceKIT team.
