A fictional community archive records an interview between an oral historian and a retired bridge worker. The guest remembers “Larkspur Bridge,” while the interviewer asks about a different place, “Larkspur Ridge.” An automatic transcript that swaps either the place name or the speaker label could change the historical record even while the text remains grammatically polished.
ElevenLabs documents Scribe v2 for prerecorded speech transcription with word-level timing, speaker diarization, audio-event labels, and keyterm prompting. These features can make review easier, but diarization assigns speaker clusters rather than proving a person’s identity, and proper-name prompting is not a substitute for listening. This guide uses imagined dialogue and no uploaded archival audio.
Secure recording permission and define the transcript purpose
Before uploading any actual oral-history material, confirm recording permissions, interviewee consent for third-party processing, access restrictions, and the intended use of the transcript. An archive copy may require verbatim wording and documented pauses, whereas a public caption track may prioritize readability. Do not mix those editorial products without labeling them.
Assign a neutral recording ID and protect private contact information. Decide which people can hear the source audio and who may change the master transcript. A speech model cannot infer permission from the existence of a file. If the material contains restricted stories or sensitive third-party details, follow institutional policy rather than treating an upload button as authorization.
Prepare a reference list of unusual names and terms
Create a short glossary from verified archival sources: Larkspur Bridge, Larkspur Ridge, equipment names, and any local organization mentioned in the approved project scope. ElevenLabs documents keyterm prompting in Scribe v2 to help with uncommon terms. Use only genuinely verified candidates and do not add speculative names that might bias the output toward an incorrect phrase.
When a word is uncertain in the audio, mark it for later review rather than silently forcing the transcript to match the glossary. The glossary assists recognition; it does not establish where a speaker worked or whether a place existed. The example locations here are fictional and should not be read as historical assertions.
Select file transcription and relevant output options
ElevenLabs’ product guide describes uploading audio or video through its Speech to Text interface, choosing detection or a known language, and then viewing and exporting results. The developer documentation also describes timestamps, diarization and other processing options. Check current plan permissions, upload limits, and retention controls for the actual workspace.
For a two-person interview, diarization can help separate turns, while word-level timestamps support targeted listening. Audio-event tags can distinguish laughter or other non-speech events from spoken assertions. Decide whether a verbatim record is necessary before applying any cleanup that removes hesitations; editing fluency can erase meaningful uncertainty in oral history.
Use diarization as a cue, not a signed identity
A generated transcript may label speech as Speaker 1 and Speaker 2. That is a clustering result, not proof that Speaker 1 is the interviewer throughout the recording. Compare the first clear self-introductions, voice turns, and overlaps against the audio. Note where the model changes labels unexpectedly or fails to distinguish simultaneous voices.
Keep the original speaker IDs and a separate human-reviewed mapping to roles or names. If a section is too overlapped to resolve confidently, use a documented “speaker uncertain” annotation. Never infer a personal identity from voice characteristics alone. The visualization’s swapped labels illustrate an error type, not a reported Scribe v2 misidentification.
Check every proper noun at its word timestamp
Word-level timestamps allow a reviewer to revisit a specific phrase in the original audio. Play the section containing “Larkspur Bridge” at natural speed and in surrounding context. Check the consonants and the interviewer’s next question before accepting the place name. Use existing archive records as a separate cross-check, but do not overwrite what was actually said with a fact from a database.
A proper transcript may preserve an interviewee’s mistaken place name and add an editorial footnote. It should not make the speaker appear to have said a corrected name. Keep the source quote separate from historical verification. Where audio remains ambiguous, mark the word as uncertain and record the time range for another reviewer.
Compare a plausible draft with an audio-backed correction
In the hypothetical bad transcript, the line “I worked at Larkspur Bridge” appears under the interviewer’s name. In the reviewed version, the audio supports assigning the line to the guest, and an editor documents the correction. The two diagram states are constructed examples; we have not observed Scribe produce either.
Use a revision log that records the original transcript line, proposed correction, evidence timestamp, editor, and uncertainty. A polished export without this trail is difficult to audit later. Do not erase the first machine transcript merely because a clean public version exists. Curators may need the original to understand how an editorial decision was made.
Design public captions separately from archival transcripts
For accessibility captions, review timing, reading speed, speaker identification, punctuation, and whether meaningful sound events need descriptions. If the output supports an export format, check the actual playback rather than trusting a text-only preview. A caption file can be factually faithful yet unusable if speaker changes appear on the wrong screen.
For the archival master, retain verbatim evidence of pauses, unclear phrases, and emotionally meaningful hesitations when appropriate. Publish a caption version only with necessary consent and disclosure of substantive editorial changes. Never use a generative cleanup feature to rewrite a participant’s testimony into a stronger claim than the recording supports.
Report methodology without inventing accuracy statistics
We consulted ElevenLabs’ official Scribe v2 overview, product transcription guide, and announcement for supported capabilities. We did not upload an interview, time-test the interface, or calculate word error rates or diarization quality. Claims about named locations and speaker attribution are fictional demonstrations, not benchmark results.
A real evaluation should use consented recordings and independent human reference transcripts, score names and speaker attributions separately from general words, and document the recording conditions. Preserve disagreements in ambiguous speech rather than declaring one machine output definitive. Costs, language coverage and processing options should be reconfirmed at the point of use.
How We Research
On November 5, 2026, we reviewed ElevenLabs Documentation: Transcription overview; ElevenLabs Documentation: Speech to Text product guide; ElevenLabs: Introducing Scribe v2. We compared the described features and access limits with our specific illustrative task and checked each proposed control against vendor guidance. We did not access a paid account, run a model, upload real media, or measure product performance. Product menus, usage limits, data handling and availability may change. The invented scenarios and drawings are clearly editorial illustrations, not screenshots or real-world test evidence.
Frequently Asked Questions
Diarization groups speaker turns; human review is needed to associate them with named interview participants.
No. It guides recognition but the relevant word must still be checked against the recording.
Yes. They help reviewers jump to the phrase and listen in context before approving an edit.
Not for an archival workflow. Preserve the source transcript and document editorial changes separately.
No. The speakers, bridge names, and example transcript lines are invented instructional material.