Audio transcription with timestamps: How word timing works

ElevenLabs describes native word-level transcription as an ASR approach that produces structured tokens for spoken words, pauses, and non-speech events, each with precise timestamps and speaker information, avoiding the secondary forced-alignment step required by conventional block-text transcription. Its Scribe model can identify events such as laughter, applause, music, and footsteps, improving contextual richness while supporting searchable archives, captioning, compliance auditing, sentiment analysis, and automated video clipping. The system can transcribe up to five audio channels independently, supports real-time WebSocket streaming with timestamp and language-detection options, and handles simultaneous speakers through deterministic channel-based speaker IDs. Outputs can be exported as JSON for applications, SRT or VTT for subtitles, and TXT, DOCX, or PDF for human-readable records, while word-level timing can also support sensitive-entity detection and real-time redaction.

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