AI Transcription

Analyze

Upload audio and receive a faithful transcript — speaker diarization, keyterm biasing, and 99-language coverage powered by dedicated speech-to-text models. No invented dialogue.

From messy audio to quotable text

Dedicated speech-to-text models — no hallucinated dialogue.

Upload your recording and use `elevenlabs-scribe-v2` (default) for multilingual transcription, speaker diarization, and vocabulary hints. `elevenlabs-scribe-v1` is also available. Review the transcript against your recording before quoting it.

How to get a transcript you can actually quote

Four choices that change the output dramatically.

1. Upload the cleanest audio file you have — m4a from voice memos beats lossy YouTube rips by a wide margin.
2. Leave language on auto-detect unless you know the answer; explicit selection trims a small amount of inference time but is rarely the limiting factor.
3. Enable diarization only when you genuinely need speaker turns — and pick an ElevenLabs Scribe model in the picker, since other backends silently ignore the toggle.
4. Drop names, brands, and domain jargon into the vocabulary hints field; ElevenLabs Scribe v2 can use the supplied terms to guide recognition.
5. Compare available models when accuracy varies by accent or subject, and verify difficult passages against the recording.

What each model is good for

Same audio in; different trade-offs out.

elevenlabs-scribe-v2

Default — diarization and vocabulary hints

Multilingual speech recognition with word timestamps, speaker diarization, audio-event tagging, and vocabulary hints.

elevenlabs-scribe-v1

Alternative Scribe model

Multilingual speech recognition with timestamps and optional speaker diarization. Review the transcript against your audio.

Best for

Recordings that need to live longer than the meeting they came from.

• Customer interviews where exact phrasing reveals product insight
• Investor calls and earnings discussions that need quotable extracts
• Podcast episodes that need accurate show notes and chapter markers
• User research sessions where verbatim quotes anchor recommendations
• Internal all-hands recordings that benefit from a searchable text version
• Conference keynotes you want to repurpose into written posts and threads
• Educational lectures and tutorials that students prefer to skim text-first

Why dedicated transcription beats LLM 'analysis'

Speech-to-text models read audio waveforms; chat models guess.

General-purpose chat models can describe an audio file but they cannot transcribe it — when forced to, they fabricate plausible-sounding dialogue that fits the topic. Dedicated speech-to-text models (ElevenLabs Scribe, Whisper) decode the actual waveform into phonemes and tokens, then assemble text from what was literally said. The failure mode is missed words on muddy audio, never invented quotes. Pair this tool with the AI Document Reviewer when you need decisions and action items extracted from the resulting transcript — keep transcription and summarization as separate steps so each is auditable on its own.

Pro tips for cleaner transcripts

Habits that compound across hours of audio.

1. Record at higher bitrates when possible — even slight quality bumps cut transcription errors materially.
2. Use vocabulary hints aggressively for any name, brand, or piece of jargon that matters; the recognizer drift is real on proper nouns.
3. For multilingual recordings with code-switching, leave language on auto-detect and use Wizper or ElevenLabs Scribe — they handle 99+ languages.
4. When diarization matters, switch the model picker to `elevenlabs-scribe-v2` explicitly; other backends ignore the toggle.
5. Pair with the AI Document Reviewer to extract action items and decisions from the resulting transcript — keep transcription and summarization as separate steps.
6. For the same recording, try two models when stakes are high — disagreement points often reveal the hardest segments to verify by ear.

Transcription FAQ

Which models power it?

Upload your recording and use `elevenlabs-scribe-v2` (default) for multilingual transcription, speaker diarization, and vocabulary hints. `elevenlabs-scribe-v1` is also available. Review the transcript against your recording before quoting it.

Is the output court-grade or legally admissible?

No — always review and have a qualified human transcriber certify before legal or compliance archives. This tool helps draft, not certify.

Will it invent dialogue that nobody said?

Dedicated transcription models transcribe audio waveforms directly — they do not hallucinate text the way LLM chat models can. Quality drops on muddy audio, but the failure mode is missed words rather than invented sentences.

Can it identify speakers by name?

Diarization gives best-effort Speaker 1 / Speaker 2 labels (when you enable the toggle and pick an ElevenLabs Scribe model). It cannot identify real names from voice alone — substitute names manually from your attendee list afterward.

How does it handle multilingual recordings?

Leave language on auto-detect for genuine code-switching; Wizper and ElevenLabs Scribe handle 99+ languages with reasonable accuracy. For best results, set language explicitly when you know it — the auto-detect path adds a small amount of inference time.

What do the vocabulary hints do?

Vocabulary hints help ElevenLabs Scribe v2 recognize names, brands, and specialized terms. Other models may not support these hints.

How do I get sharper accuracy?

Cleaner audio source (record at higher bitrates, single mic per speaker), explicit language selection when known, vocabulary hints for domain jargon, and the ElevenLabs path when speaker structure matters more than raw cost. There is no shortcut around bad audio.

Searchable meetings, citable interviews

Make voice ephemeral, text permanent.

Turn recordings into something your team can grep, cite, onboard from, and reuse months later. The transcript is not the goal — the searchable, quotable, repurposable record is. Run once, verify the uncertainty list, then turn the output into briefs, posts, or product insight.