The cutting is the easy half. Trimming a 90-second clip out of a six-hour recording takes a couple of minutes in any editor you already know. The slow half is deciding which 90 seconds, and that is what eats the afternoon. So the practical order is: get a transcript of the stream, use it to shortlist candidate moments with timecodes, then cut. What follows are the real options for that middle step, including the ones where you do it by hand.
Scrub through it yourself
Open the recording, run it at 2x, keep a text file open, write down a timecode every time something lands. Then cut from the list. For one stream with two or three moments you already remember, this is still the fastest method available, because you know what you are looking for and nothing has to guess on your behalf.
The cost is that it does not compress. Six hours at 2x is three hours of attention before a single clip exists, and you cannot do anything else during it. Editors who do selection work week after week tend to describe the same split: the timeline work is quick, and choosing what goes on the timeline is where the hours disappear. If that is your week, the manual pass is what you are trying to get out of, so skip to the last two sections.
Auto-clipping services
Upload the file, get back a batch of vertical clips with captions burned in and a score attached to each. This category has gotten genuinely good at the mechanical part: detecting speech, cutting on sentence boundaries, reframing to 9:16, keeping a face centered.
What it cannot do is know what your audience cares about. The score is a proxy built from things like speech density and keyword hits, and across six hours of dead air, setup, and two people talking over each other, the proxy drifts. In practice you review all thirty suggestions, which beats watching six hours but is still a review pass with your eyes on every candidate. These tools also handle talking well and handle silence badly. If the best moment in your stream is something that happened on screen while nobody narrated it, no amount of caption-level cleverness will surface it.
Transcribe it, then search the text
Run the audio through a speech-to-text model, open the transcript, search for what you remember. This is cheap, it can run locally, and it gets you an exact timecode the moment it hits.
Two limits. You have to already know the words, so "the bit where he got annoyed about pricing" only works if someone said the word pricing. And the transcript itself is big: six hours of talking is somewhere north of fifty thousand words, which is a short novel to search blind. Keyword search rewards you for remembering the phrase and abandons you when you only remember the meaning.
Put a search layer over the footage you already have
The newer approach is to index the material once when it lands, then ask questions of it. You describe the moment in ordinary language and get back timestamps with enough surrounding context to judge whether it is the right one. It answers "where does this stop being setup and start being the actual argument" without you having guessed the phrasing in advance.
Vivu is one implementation of that layer: the stream is indexed when it arrives in your storage, so finding the six candidate moments in it is a query instead of another pass through the recording. It does not cut, caption, or reframe anything. You leave with timecodes and take them into whatever editor you already use.
The honest boundary on this category: it earns its keep across an archive, not on one file. If you stream once and will never look at this recording again after the clips ship, indexing it is overhead you do not need.
When you do not need any of this
If you were live for the stream and you remember roughly when the moment happened, drag the scrub bar. Your memory of your own broadcast is better than any retrieval system, and it is instant.
There is a second case worth naming. If your clips are always structurally the same (the cold open, the Q&A block, the last ten minutes), you do not have a search problem at all. You need a chapter marker convention and someone dropping markers live while the stream runs. That costs nothing and removes the entire selection step.
How to tell which one you are
Sort by frequency, not by length. A six-hour stream you clip once is a manual job, and any tooling you set up for it will take longer than the job. A six-hour stream every week, with an archive behind it that you keep going back into, is the case where the manual pass quietly becomes a part-time role.
Then ask the second question: when you go looking for a moment, do you remember the words or only the meaning? Remembering the words means a transcript and ctrl-F will carry you a long way. Remembering only the meaning is the thing keyword search cannot do, and it is the only reason to add a retrieval layer at all. Either way the last call, which moment is actually good, stays yours.