ElevenLabs Transcript — AI Speech-to-Text API
What is ElevenLabs Transcript?
ElevenLabs Transcript (powered by Scribe) is a state-of-the-art automatic speech recognition (ASR) model built for developers who need accurate, structured transcriptions at scale. Trained to handle real-world audio — accented speech, background noise, overlapping voices, and domain-specific vocabulary — Scribe consistently outperforms competing models including OpenAI Whisper v3 and Google Gemini Flash on word error rate benchmarks across 99 languages.
Available in two versions, scribe_v1 and scribe_v2, the model converts audio files into rich, structured transcripts complete with speaker labels, word-level timestamps, and inline audio event annotations — all through a single API call.
Key Features
- Industry-Leading Accuracy — Achieves a 96.7% accuracy rate for English and record-low word error rates across 99 languages, outperforming Google Gemini 2.0 Flash, OpenAI Whisper v3, and Deepgram Nova-3 in third-party benchmarks.
- Speaker Diarization — Identifies and labels up to 32 distinct speakers in a single recording, with a configurable diarization threshold for fine-tuned separation control.
- Word-Level Timestamps — Captures the exact start and end time of every word or character, enabling frame-accurate subtitle generation and interactive audio players.
- Audio Event Tagging — Detects and annotates non-speech sounds such as (laughter), (applause), or (footsteps) inline within the transcript for richer contextual output.
- 99-Language Support — Supports transcription across 99 languages with automatic language detection, including underserved languages like Serbian, Cantonese, and Malayalam where other models often exceed 40% error rates.
- Multi-Channel Audio — Processes up to 5 independent audio channels separately, ideal for call center recordings with distinct agent and customer tracks.
- Keyterm Prompting (scribe_v2) — Bias transcription towards up to 100 custom terms such as product names, medical terminology, or brand-specific vocabulary.
- Entity Detection (scribe_v2) — Automatically identifies and timestamps sensitive entities like names, credit card numbers, or medical conditions in the transcript.
Best Use Cases
Media and Entertainment — Generate frame-accurate subtitles and closed captions for films, YouTube videos, and streaming content. Word-level timestamps feed directly into SRT and VTT subtitle workflows.
Podcast and Interview Transcription — Diarize multi-speaker recordings automatically. Audio event tagging adds expressive context — marking moments of laughter or silence — that pure transcription misses.
Business Meeting Intelligence — Convert recorded calls, standups, and customer interviews into searchable, speaker-attributed transcripts. Pair with an LLM to extract action items or summaries.
Call Center Analytics — Use multi-channel mode to process separate agent and customer audio tracks simultaneously. Speaker diarization ensures attribution is always correct.
Medical and Legal Documentation — High accuracy on domain-specific vocabulary, combined with entity detection in scribe_v2, makes this suitable for clinical dictation and legal deposition transcription where precision is non-negotiable.
Accessibility — Generate captions for live events or archived content across 99 languages, helping creators meet accessibility requirements globally.
Prompt Tips and Output Quality
Start with scribe_v1 for general-purpose transcription — it handles most audio reliably with minimal configuration. Switch to scribe_v2 when your audio contains specialized terminology: use the keyterm prompting feature to pass up to 100 domain-specific words to the model before transcription begins.
For multi-speaker recordings, set diarize: true and provide a rough num_speakers count when known. If the model over-splits a single speaker into two, raise diarization_threshold above 0.22; if it merges two speakers into one, lower it toward 0.10.
Always set timestamp_granularity: word when building subtitle pipelines or interactive transcripts — character-level granularity is useful for specialized downstream processing but rarely needed in production.
For call center or interview audio recorded with separate microphone channels, enable use_multi_channel to ensure each speaker is processed on their own track, dramatically improving diarization accuracy.
Keep temperature at 0 for deterministic, production-grade output. Use a non-zero value only when experimenting with noisy or low-quality audio.