ElevenLabs Scribe v2 (2026): transcription at scale for captions, clips and QA

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What is Scribe v2?

Scribe v2 is a transcription workflow designed for creators, teams, and businesses that handle audio at scale: long recordings, mixed speakers, content repurposing, and fast QA.

The main idea is simple: if transcription is part of your “content stack”, you want it to be predictable (stable on long audio), brand-aware (names and product terms), and reviewable (spot sensitive entities quickly).

If you want to test Scribe workflows end‑to‑end, try ElevenLabs here.

When to use it

Scribe v2 is most useful when transcription is not the final output, but the starting point for multiple deliverables:

• Captions for YouTube, courses, and product demos
• Short clips (find the best moments, then cut)
• Searchable knowledge (projects, libraries, “what did we say?”)
• QA & governance (review sensitive content faster)

If you only need a one-off transcript, any tool can work. But if you do transcription weekly (or daily), the workflow details matter.

Keyterm prompting

Keyterm prompting lets you bias transcription toward specific terms using context-aware prompting. It’s designed for situations where generic transcription misses what matters most:

• Brand names (including unusual spellings)
• People names and company names
• Acronyms, product SKUs, feature names
• Technical vocabulary (AI, audio, compliance)

The partner note we received highlights up to 100 custom terms for this workflow.

How to use it (practical method)

1. Build your “always-on” list (10–30 terms).
   • Your product/brand names
   • Your top 10 competitors
   • Common acronyms you say out loud
2. Add episode/project terms (10–50 terms).
   • Guest names, event names
   • Cities, customer names (if allowed)
   • Internal project codenames
3. Write short context lines so the model understands the terms (not just the string).
   • Example: “ElevenLabs Studio”, “Scribe v2”, “Projects”, “Voice Library”
4. Run a 60–90 second test first.
   • Listen for the hardest part: names + acronyms + fast speech
5. Lock a versioned list once it performs.
   • Store it like a preset: terms_v3_2026-03 so your team stays consistent.

Keyterm prompting should reduce the “death by a thousand fixes” problem: manually correcting the same product term across dozens of transcripts.

Entity detection

Entity detection is about speed and safety. It can automatically detect and timestamp sensitive or important entities such as:

• Names
• Credit card numbers
• Medical conditions
• SSNs

The note also mentions that entity detection supports 56 entity types, and that it’s available in Scribe v1 as well.

How to use entity detection in real workflows

• Redaction workflow: detect → review → redact before sharing with clients or publishing.
• Compliance review: generate a “review list” of entities that need human sign‑off.
• Editing workflow: jump straight to timestamped names/terms to verify correctness.

Entity detection doesn’t replace policy. It replaces busywork: it gives reviewers a shortlist of “look here” timestamps instead of asking them to scan an hour of audio.

Smart multi-language audio

If your content includes multiple languages (a common case in interviews, webinars, or global teams), Scribe v2 can automatically detect and transcribe multiple languages within the same file.

Practical tips:

• Keep clean speaker turns when you can (it improves punctuation and segmentation).
• Use keyterm prompting for mixed-language brand terms (names are often the first thing to break).
• QA the first 3–5 minutes; if it fails early, don’t waste an hour.

Captions workflow: transcript → SRT/VTT

Most teams underestimate how much caption quality depends on the transcript workflow.

Here’s a repeatable pipeline:

1. Transcribe the audio (Scribe v2).
2. Clean the text:
   • Remove filler that hurts readability
   • Fix brand terms and names (use keyterms)
   • Standardize numbers and punctuation
3. Generate captions (SRT/VTT).
4. Timing review:
   • Spot check fast segments and cuts
   • Keep lines short (mobile readability)
5. Export & archive:
   • Save a “source” version before any retiming

If you also generate voiceovers, pair this with the full workflow page (script → TTS → cleanup → captions → QA).

QA and governance (the boring part that saves you)

When you scale production, problems scale too. The fastest teams win by building a small QA habit:

• Quality QA: pronunciation, speaker names, jargon, timing
• Brand QA: consistent terminology across videos
• Risk QA: sensitive entities flagged and reviewed

Entity detection is a lever here: it makes QA faster because reviewers aren’t guessing where the risks are.

“Content stack reset” angle (why this works in 2026)

The best productivity gains often come from removing tool clutter. A single platform that covers:

• Voice creation (TTS / core voices)
• Consistency (voice library)
• Organization (projects)
• Transcription (Scribe v2)

…can replace a patchwork of “one tool per task” workflows.

If you want to run a real “stack reset” test this month, try ElevenLabs here on a 10–15 minute recording and measure how much time you save on rework.

If you’re rebuilding your 2026 stack, the best test is simple: run one real project through the workflow, then measure time saved on rework (names, redactions, caption drift, long silences).