How does this client-side transcript filter isolate broadcast segments and compute matching metrics?
The indexer operates entirely on a local four-segment fixture stored in memory. When you supply a search term or click a speaker chip, computeState evaluates each segment against active filters: it checks for an exact speaker match, performs a case-insensitive substring search across segment text and speaker labels, and counts keyword occurrences by splitting matching text strings. The filtered subset then updates the metric summary, re-renders the transcript cards, and generates a formatted JSON export preview.
This interface does not query live ABC News broadcast feeds or a remote API. The archive records, timestamps, and Reddit source tags are hardcoded demonstration objects of fixed 48-second length. Substring counting uses string delimiter splitting rather than natural language tokenization, so partial word matches are treated as complete hits.
Type 'mistakes' into the Keyword Search box. The matching segments counter immediately shifts from 4 / 4 down to 1 / 4, the keyword occurrences counter changes to 1, and the Pickering Admissions metric reflects 1. The transcript list filters out the Anchor and Reporter segments, displaying only Warren Pickering's segment with the word 'mistakes' highlighted.
The search listener captures user input on every input event and passes the normalized lowercase string to computeState. The array filter requires both the speaker condition and the keyword condition to be satisfied. If a query matches either the segment body or speaker name, occurrence counts are derived by splitting the segment string on the query token and subtracting one.
The JSON export function assembles metadata including slug identifier, extracted timestamp, filter parameters, and the filtered transcript array into a Blob object with type application/json. It triggers a client-side download via a transient anchor element without contacting any server.
This saved archive filters four stored records. It does not retrieve a live broadcast. The query is trimmed and lowercased, then tested as a substring in either the text or speaker label. Searching anchor returns the two records with that speaker label, so the count changes from four to two. The bars use one hundred pixels per record: four hundred for the original set and two hundred for the match set. The displayed keyword metric also becomes two, because a speaker-only match contributes at least one even with zero occurrences in the body. That metric is not a literal text-occurrence count in this case. The speaker filter is applied first, and the query must match as well. Selecting Anchor leaves two records. Adding the query state leaves one, so these are intersecting conditions rather than alternatives. A literal dot star query finds zero; the search is substring matching, not a wildcard regular expression. When nothing matches, the empty-state reset button clears both controls and restores all four. The actual JSON download includes filter state, metrics and the filtered records. The preview uses different field names but represents the same selected set. The stored timestamps are zero, fifteen, thirty two and forty eight seconds. Our line uses eight pixels per second, with gaps one hundred twenty, one hundred thirty six and one hundred twenty eight pixels. The final timestamp is a record start; it does not establish the end of a real clip or the duration of the last statement. The interface keeps a fixed zero zero forty eight duration label under every filter. Its uploaded eight minutes ago and live connected labels are also saved strings. News content and transcript authenticity remain unverified. This local video explains the implementation without repeating or validating the political assertions in the stored text.