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 an audio file and route it through a real transcription model — `gpt-4o-transcribe` for OpenAI-grade accuracy, `elevenlabs-scribe-v2` when you need speaker diarization and keyterm biasing, `wizper` for fast multilingual coverage, or `fal-speech-to-text` for the cheapest English-leaning runs. Pick a language (or let auto-detect handle it), enable speaker diarization if you need turns, and seed vocabulary hints with the names, brands, and jargon you want the model to lock in. The transcript comes back word-for-word from the audio — no LLM cleanup, no invented sentences, no smoothed-over silence.

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 and OpenAI Transcribe both bias the recognizer toward the supplied terms.
5. Switch models if accuracy disappoints on a particular accent or domain — the four backends fail differently, and one will usually clear what another stumbled on.

What each model is good for

Same audio in; different trade-offs out.

gpt-4o-transcribe

Default — flagship accuracy

OpenAI's GPT-4o speech model. Best general-purpose accuracy and word-error-rate; supports vocabulary hints via the prompt parameter.

elevenlabs-scribe-v2

Diarization + keyterms

Word-level timestamps, speaker diarization, audio-event tagging ([laughter], [applause]), and keyterm biasing across 99 languages.

wizper

Fast multilingual

Whisper Large v3 optimized by Fal — same accuracy as OpenAI Whisper at ~2x the speed. Optionally translates to English in one pass.

fal-speech-to-text

Cheapest English

NVIDIA Canary on Fal. Lowest cost per minute with built-in punctuation and capitalization — best when English clarity matters more than diarization.

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 (`gpt-4o-transcribe`, 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?

Dedicated speech-to-text models — `gpt-4o-transcribe` (default, OpenAI flagship), `elevenlabs-scribe-v2` (multilingual, diarization, keyterm biasing), `wizper` (Whisper Large v3 on Fal — fast, multilingual translation), and `fal-speech-to-text` (NVIDIA Canary — cheapest, English-leaning). All read your uploaded audio directly; switch when one struggles with a particular accent or domain.

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?

Comma-separated names, brands, and jargon are biased into the recognizer. ElevenLabs Scribe v2 reads them as keyterms; OpenAI Transcribe reads them as the `prompt` parameter. Both improve accuracy on tricky proper nouns. Other models ignore the hints silently.

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.