← ElevenLabs Interview Insights
My first instinct was to just poll the audio element's currentTime on a short interval and map it against word timestamps.
Start by clarifying requirements: is this for real-time streaming or pre-recorded audio? Then outline a data model that maps time ranges to text segments, and describe how to update highlights efficiently using audio timeupdate events or requestAnimationFrame. Finally, discuss trade-offs between accuracy, performance, and complexity, and how you'd handle edge cases like seeking and variable playback speed.
Pro tip: Mention that you'd use binary search on sorted timestamped segments to find the active segment in O(log n), and throttle DOM updates to avoid jank—this shows you think about both algorithmic efficiency and real-world browser performance.
Ask whether the audio is streaming or pre-loaded, if word-level or segment-level highlighting is needed, and what platforms (web, mobile) are targeted. This determines the sync strategy and data structures.
Represent the transcript as an array of segments with start and end times (e.g., {text, startTime, endTime}). For word-level sync, use a finer-grained array. Ensure timestamps are in a consistent unit (e.g., seconds).
Use the audio element's timeupdate event or requestAnimationFrame to poll currentTime. Find the active segment via binary search and update the highlight. Throttle updates to ~60fps or less to avoid performance issues.
Account for seeking, playback rate changes, buffering, and audio ending. On seek, immediately recalculate the active segment. For variable speed, adjust the polling frequency or use interpolation.
Compare event-driven vs. polling approaches, and consider using Web Audio API for more precise timing. Mention memoization, virtual scrolling for long transcripts, and accessibility (e.g., ARIA live regions).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.