npx skills add ...
npx skills add elevenlabs/skills --skill speech-to-text
Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio/video to text, generating subtitles, transcribing meetings, or processing spoken content.
npx skills add elevenlabs/skills --skill speech-to-text
Transcribe audio to text with Scribe v2 - supports 90+ languages, speaker diarization, and word-level timestamps.
Setup: See Installation Guide. For JavaScript, use
@elevenlabs/*packages only.
| 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 |
scribe_v2_realtime_turbo | Realtime transcription variant | Live transcription |
scribe_v2_realtime_lite | Realtime transcription variant | Live transcription |
Word-level timestamps include type classification and speaker identification:
Identify WHO said WHAT - the model labels each word with a speaker ID, useful for meetings, interviews, or any multi-speaker audio:
For call recordings, the batch API can label diarized speakers as agent and customer by setting detect_speaker_roles=true alongside diarize=true. This option is not compatible with use_multi_channel=true.
If your workspace has registered speaker profiles, set use_speaker_library=true with diarize=true to match detected speakers against the speaker library.
Use use_multi_channel=true when each speaker is isolated on a separate audio channel. By default, the API returns one transcript per channel under transcripts; set multichannel_output_style="combined" to receive one transcript merged by timestamp, with channel_index on each word.
Help the model recognize specific words it might otherwise mishear - product names, technical jargon, or unusual spellings (up to 100 terms):
Automatic detection with optional language hint:
Audio: MP3, WAV, M4A, FLAC, OGG, WebM, AAC, AIFF, Opus Video: MP4, AVI, MKV, MOV, WMV, FLV, WebM, MPEG, 3GPP
Limits: Up to 5.0GB file size, 10 hours duration
Word types:
word - An actual spoken wordspacing - Whitespace between words (useful for precise timing)audio_event - Non-speech sounds the model detected (laughter, applause, music, etc.)Common errors:
Monitor usage via request-id response header:
For live transcription with ultra-low latency (~150ms), use the real-time API. The real-time API produces two types of transcripts:
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.
| 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 |
Set includeLanguageDetection: true to receive the detected language code in delayed final
transcript events.
| Event | Description |
|---|---|
partial_transcript | Live interim results |
final_transcript | Stable segment result sent before the segment is committed |
final_transcript_with_timestamps | Delayed final result with timestamps and/or detected language |
committed_transcript | Final results after commit |
committed_transcript_with_timestamps | Final with word timing |
committed_transcript_entities | Entities detected in a committed segment |
invalid_request | Connection parameters were rejected and the session closes |
error | Error occurred |
See real-time references for complete documentation.