Transcribe
Convert spoken audio to text with optional speaker diarization and audio event tagging.
Overview
The Transcribe node uses ElevenLabs Speech-to-Text to convert audio into a text transcript. It supports automatic language detection or explicit language selection, speaker diarization (identifying who said what), and audio event tagging (labeling non-speech sounds like music, laughter, or applause). The output includes the full transcript text as well as per-segment results with timestamps.
Configuration
| Field | Type | Default | Description |
|---|---|---|---|
| Provider | TranscribeProvider |
"elevenlabs-stt" |
Transcription engine |
| Language | string |
"auto" |
Language code for the audio, or “auto” for automatic detection. Supports 20+ languages |
| Speaker Diarization | boolean |
false |
When enabled, identifies and labels different speakers in the transcript |
| Tag Audio Events | boolean |
false |
When enabled, annotates non-speech audio events (music, laughter, applause, etc.) in the transcript |
Inputs & Outputs
- Input:
in– audio file to transcribe - Output:
text– full transcript text string
Output Details
The node produces both a simple text output and structured result data:
| Field | Type | Description |
|---|---|---|
| generatedText | string |
The full transcript as plain text |
| generatedResults | array |
Array of result objects, each containing text, language, jobId, and timestamp |
When Speaker Diarization is enabled, the transcript includes speaker labels (e.g., “Speaker 1:”, “Speaker 2:”) before each segment.
When Tag Audio Events is enabled, non-speech sounds are annotated inline (e.g., “[music]”, “[laughter]”).
Best Practices
- Use auto-detect for language unless you know the audio is in a specific language. Explicit language selection can improve accuracy for languages that sound similar.
- Enable Speaker Diarization when the audio contains multiple speakers (interviews, meetings, podcasts) to get labeled segments.
- Enable Tag Audio Events when the audio context matters (e.g., transcribing a video where background sounds are relevant to understanding).
- For best accuracy, use clean audio. Consider running the Voice Extractor node upstream if the source has significant background noise.
- Shorter audio segments transcribe more reliably. For very long audio, consider splitting into segments first.
Common Use Cases
- Transcribing interview or podcast audio for written content
- Generating subtitles and captions from video audio tracks
- Converting voice memos or meeting recordings to text
- Creating searchable text archives from audio libraries
- Feeding transcripts into downstream AI nodes for summarization or analysis
Tips
- The output connects to any text-consuming node. Common downstream connections include Generate Text (for summarization), Combine Text (for assembly), and Add Captions (for subtitle generation).
- Speaker diarization and audio event tagging are independent options – you can enable one, both, or neither.
- The transcription is processed asynchronously via the backend worker queue. Progress is shown in the node during execution.
- Language auto-detection works across the full set of supported languages. The explicit language dropdown provides 20+ language options matching the ElevenLabs STT model’s capabilities.
- For word-level timestamps (rather than segment-level), use the Forced Alignment node with the transcript output of this node.