AI for Automated Video Captioning

Generate accurate, accessible captions at scale with AI-driven speech

Automated captioning converts speech to text and aligns it to video for accessibility, engagement, and search. Modern systems combine ASR, diarization, language models for punctuation/casing, and domain adaptation.

High caption accuracy, fast turnaround, accessibility compliance (e.g., WCAG), multilingual support, and consistent style.

Confidence scoring, human-in-the-loop for low confidence, glossary inj

TV, streaming, and podcasts with speaker changes and multiple languages.

Fast turnaround with on-brand styling and emoji/hashtag support.

live demo · related simulation● LIVE

Pilot: Run batch captioning on a content slice; measure WER and user f

Scale: Add live captioning, human review workflows, multilingual support, and automated QA checks before publishing.

Ingest audio/video from CMS; process audio tracks; apply glossaries; generate SRT/VTT and store transcripts. Expose APIs and webhooks for completion; integrate with editing tools for correction and approvals.

Frequently asked questions

What metrics should be monitored when evaluating automated captioning performance?

Monitor WER, timing errors, and hallucinations; regression tests for new models.

How does compliance with WCAG/ADA regulations factor into the captioning process?

WCAG/ADA timing rules, speaker cues, and export formats; audit trails for edits.

What data should be tracked to assess the efficiency of human review workflows?

Track human edit effort, glossary hit rate, live latency, and user satisfaction for accessibility.

How does domain-specific adaptation improve captioning accuracy?

Domain-tuned ASR and glossary kept WER low and reduced manual edits.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation