Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio/video to text, generating subtitles, transcribing meetings, or processing spoken content.

ElevenLabs Speech to Text Transcribe audio to text with Scribe v2 supports 90+ languages, speaker diarization, and word level timestamps. Setup: See [Installation Guide](references/installation.md). For JavaScript, use @elevenlabs/ packages only. Quick Start Python JavaScript cURL Models Model ID Description Best For scribe v2 State of the art accuracy, 90+ languages Batch transcription, subtitles, long form audio scribe v2 realtime Low latency (~150ms) Live transcription, voice agents Transcription with Timestamps Word level timestamps include type classification and speaker identification: Speaker Diarization Identify WHO said WHAT the model labels each word with a speaker ID, useful for meetings, interviews, or any multi speaker audio: Keyterm Prompting Help the model recognize specific words it might otherwise mishear product names, technical jargon, or unusual spellings (up to 100 terms): Language Detection Automatic detection with optional language hint: Supported Formats Audio: MP3, WAV, M4A, FLAC, OGG, WebM, AAC, AIFF, Opus Video: MP4, AVI, MKV, MOV, WMV, FLV, WebM, MPEG, 3GPP Limits: Up to 3GB file size, 10 hours duration Response Format Word types: word An actual spoken word spacing Whitespace between words (useful for precise timing) audio event Non speech sounds the model detected (laughter, applause, music, etc.) Error Handling Common errors: 401 : Invalid API key 422 : Invalid parameters 429 : Rate limit exceeded Tracking Costs Monitor usage via request id response header: Real Time Streaming For live transcription with ultra low latency (~150ms), use the real time API. The real time API produces two types of transcripts: Partial transcripts : Interim results that update frequently as audio is processed use these for live feedback (e.g., showing text as the user speaks) Committed transcripts : Final, stable results after you "commit" use these as the source of truth for your application A "commit" tells the model to finalize the current segment. You can commit manually (e.g., when the user pauses) or use Voice Activity Detection (VAD) to auto commit on silence. Python (Server Side) JavaScript (Client Side with React) Commit Strategies Strategy Description Manual You call commit() when ready use for file processing or when you control the audio segments VAD Voice Activity Detection auto commits when silence is detected use for live microphone input Event Types Event Description partial transcript Live interim results committed transcript Final results after commit committed transcript with timestamps Final with word timing error Error occurred See real time references for complete documentation. References [Installation Guide](references/installation.md) [Transcription Options](references/transcription options.md) [Real Time Client Side Streaming](references/realtime client side.md) [Real Time Server Side Streaming](references/realtime server side.md) [Commit Strategies](references/realtime commit strategies.md) [Real Time Event Reference](references/realtime events.md)

speech-to-text