Workflows
Video Transcript Search: Find Any Spoken Word Across Your Entire Library
Video transcript search turns spoken words into searchable text with word-level timestamps. How automatic transcription, BM25 ranking, and speaker diarization make every sentence in your footage findable.
You know someone said "we should push the launch to Q3" during a stakeholder interview. You do not know which interview, which camera, or which timecode. Without video transcript search, your options are rewatching everything or asking around and hoping someone remembers.
Video transcript search eliminates that guessing. Every word spoken in your footage becomes searchable text, timestamped to the exact moment it was said. You type a phrase, get a list of results, and click to jump straight to the timecode.
What video transcript search actually is
Video transcript search is the combination of two processes: automatic speech-to-text transcription and full-text indexing. First, an AI model converts the audio track of every video in your library into timestamped text. Then a search engine indexes that text so you can query it instantly.
The result is that every sentence ever spoken on camera becomes as searchable as an email. You do not need to have been on set, know the filename, or remember which folder the clip is in. If someone said it, you can find it.
Welcome everyone, thank you for joining us today for our annual company conference.
Before we dive in, I want to acknowledge the incredible work everyone has done this past year.
Our company has grown by 35% year-over-year and we have expanded into three new international markets.
The migration patterns shifted dramatically. We tracked over two hundred species across the delta.
How automatic transcription works
Modern speech-to-text models process audio and produce transcripts with word-level timestamps. That means each individual word has a start time and end time associated with it, not just each sentence or paragraph.
Word-level timestamps matter because they enable precise navigation. When you click a search result, you land on the exact second the phrase was spoken, not somewhere in the general vicinity. For a 45-minute interview, the difference between "somewhere in the middle" and "22 minutes and 14 seconds in" is significant.
FrameQuery's transcription pipeline handles over 50 video formats natively, including R3D, BRAW, ProRes, MXF, and CinemaDNG. You do not need to transcode or extract audio manually. Point it at your footage and the transcription happens automatically.
How BM25 ranking surfaces the best matches
Having a transcript is only useful if the search engine can rank results intelligently. FrameQuery uses BM25, the same ranking algorithm that powers most modern search engines, to determine which results are most relevant to your query.
BM25 considers two factors: how often your search terms appear in a given transcript segment (term frequency) and how rare those terms are across your entire library (inverse document frequency). A segment that mentions "quarterly revenue projections" scores higher than one that only mentions "quarterly" if your query is "quarterly revenue projections." A term that appears in every single video is weighted less heavily than a term that appears in only a few.
This means common words do not drown out meaningful matches. If you search for "the product launch timeline," the ranking prioritizes segments where "product," "launch," and "timeline" appear together, not every segment where someone said "the."
Speaker diarization adds attribution
Transcription tells you what was said. Speaker diarization tells you who said it. During processing, an AI model analyzes the audio and segments the transcript by distinct speakers. Each sentence is tagged with a speaker identity: Speaker 1, Speaker 2, Speaker 3.
You can name these speakers in FrameQuery, turning generic tags into recognizable identities. Once named, speaker identities persist across your entire library. If the same person appears in multiple recordings, their voice profile carries across all of them.
This enables speaker-filtered search. Instead of finding every mention of "budget concerns" across all speakers, you can filter to find only the moments where your CFO discussed budget concerns. For multi-speaker content like interviews, meetings, and panel discussions, this turns a broad search into a precise one.
Exact phrase matching
Searching for individual words is useful but sometimes you need an exact phrase. Wrapping your query in quotes triggers exact phrase matching: "we need to revisit the timeline" finds only segments where those exact words appear in that exact order.
This is critical for locating specific quotes. If a subject said something memorable during an interview and you want to find that precise statement, phrase matching eliminates the noise from partial word matches.
Practical examples
Finding a specific quote. A documentary subject made a powerful statement about immigration policy during one of twelve interview sessions. Search for the key phrase in quotes and find the exact session and timecode in seconds.
Locating every discussion of a topic. A legal team needs every instance where "non-compete" was mentioned across 80 hours of deposition footage. A single search returns every occurrence, timestamped and ready for review.
Pulling interview highlights. A podcast producer needs the best moments from a two-hour conversation. Searching for the key topics discussed surfaces the strongest segments without requiring a full relisten.
Compiling training material. An L&D team wants every instance where a trainer explained "onboarding process" across a year of recorded sessions. Transcript search finds them all, across every recording, in one query.
How this differs from YouTube search
YouTube's search bar searches video titles, descriptions, tags, and channel names. It does not search the actual spoken content of videos. If someone said something important but the video title is "Team Meeting - March 14," YouTube search will never connect your query to that content.
Video transcript search is fundamentally different because it searches what was said, not what someone wrote about the video. This distinction matters enormously for any library where metadata is sparse, inconsistent, or nonexistent, which describes most production footage.
What gets indexed and where
FrameQuery stores the complete transcript index locally on your machine using Tantivy, a Rust-based search engine. The index is compact (typically a few megabytes per hour of video) and searches are instant because they never leave your device. No internet connection required after processing, no per-query costs, no footage uploaded to third-party servers.
Every word, its timestamp, its speaker attribution, and its position in the transcript are all indexed. The search engine can query across your entire library in milliseconds, regardless of whether you have ten hours or ten thousand hours of footage indexed.
Join the waitlist to search every word spoken in your footage when FrameQuery launches.