Help: Auto-captions are misaligning with scene changes. How to fix?

Hey! First, welcome to the community. Editing tutorial content, especially on something like Airflow, is a fantastic use case for Descript's workflow.

You're definitely not alone with this misalignment. The root cause is that the scene detection (visual) and the transcription/word timing (audio) are two separate, independent analysis passes. They don't get "locked" together by default, which is why a sentence can get bisected by a scene marker.

Your process is standard, but there's a crucial mindset shift: think of the scene markers as *suggestions*, not a rigid structure. Here's what I do:
- After generation, I scan the timeline and *manually adjust the scene change markers* to align with natural pauses or the end of a caption block. You can just drag them.
- Alternatively, you can sometimes get better alignment by regenerating the transcript *after* you've tweaked the scene markers to where you actually want your cuts, though that's more of a trial-and-error process.

For the "Remove filler words" feature, it works off the word-level transcript timing, not the scenes. So that part should still function even with misaligned scenes, but it *feels* broken because the visual cuts are in awkward places. Getting those scene markers into the right spots first makes the whole editing flow much smoother. It's a bit of upfront work that saves a ton of frustration later.

The advice to treat scene markers as suggestions is correct, but it misses the real issue. You shouldn't have to fix a broken feature you're paying for.

You said your process is to drop the video in and let it generate everything. That's the problem. You're trusting two separate, uncoordinated algorithms to magically agree. They won't. The workflow is fundamentally flawed for any edit that depends on sync between audio and visual cuts.

My advice is to pick one master track and ignore the other. If you're editing for "ums" and filler words, your audio transcript is your source of truth. Use the caption blocks to make your cuts, then delete or ignore the scene change markers altogether. They're just visual noise for your use case.

Spending time manually dragging scene markers to align with captions is a waste of effort. The software should do that, and until it does, treat it as a bug, not a workflow step.

Hey, welcome! I've run into this exact same friction when editing my own pipeline walkthroughs. That "editing like a doc" concept breaks down a bit right here, because you're dealing with two separate data streams - the audio transcript and the visual scene detection. They're generated independently, like two unsynchronized event logs.

Your process is fine, but you need to pick one as your primary partition key, so to speak. Since you're focused on removing pauses and filler words, the audio transcript (captions) *has* to be your source of truth. Try this: ignore the scene markers at first. Use the caption track to make all your cuts and deletions for the audio. *Then*, go back and review where the scene markers landed. Often, you can just delete the ones that are now in awkward spots, because your visual cuts might have shifted.

It's a bit like dealing with late-arriving data - you process what you know, then handle the drift. Annoying, but you can make it work.

Your process isn't wrong, but it assumes a level of integration between features that doesn't exist. The previous replies are correct about picking a source of truth, but they're treating this as a workflow tip when it's actually a design limitation in the product's automation.

The core issue is that you're using two separate, non-communicating AI models - one for acoustic event detection and one for visual shot detection. They're optimized for different tasks and run in parallel, not sequence. There's no "lock" function because the system isn't built to create a temporal relationship between the two data streams.

For your specific use case, you should invert your workflow. Generate the transcript first and perform your audio-based edits. *Then*, run the scene detection. The scene detection algorithm will analyze the *resulting* video timeline, which now has your audio cuts, and place its markers accordingly. This creates a dependent relationship, with the visual analysis subordinate to your audio edits, which is logically consistent for tutorial content.

It's less about fixing misalignment and more about imposing a correct order of operations on independent processes.

Ah, that's a smart way to frame it - imposing an order of operations on independent processes. You're right, it's treating them like two separate CI/CD pipelines that shouldn't run concurrently if the output of one feeds into the other.

I've found the same trick works in reverse if your primary edit is visual. Do your scene cuts first, *then* run the transcription. The audio analysis will run on the chopped-up segments, which can actually improve caption accuracy for the bits you kept.

It does feel like a workaround for a feature that should have a "sync" toggle, though. Reminds me of trying to get log timestamps from different servers to line up before I built a proper collector.

That's a helpful technical analogy, comparing it to unsynchronized log streams. Spot on.

Your point about the order improving accuracy is key - running transcription on pre-cut segments means the model isn't wasting cycles analyzing audio you've already decided to delete. That's not just a workaround, it's a genuine efficiency gain.

The desire for a "sync toggle" is understandable, but I'm cautious about it. Forcing two independent detection models into lockstep could degrade the performance of both, or create a brittle dependency. Sometimes treating features as separate, composable tools is the right design, even if it requires a bit of user orchestration.

I think you've hit on the core of it with your question about order. The step you're missing is between step 2 and 3.

You're doing everything at once, which means the two models are stepping on each other's work. Treat your edit like a data pipeline: one job at a time.

Since your main goal is cutting filler words, run the transcript first. Make all your audio cuts and deletions using the captions as your guide. *Then*, run the scene detection feature on your newly trimmed timeline. The scene markers will now be based on your final visual segments, so they'll be in logical places. It adds one extra click, but it saves the headache of manual realignment.

It's a mindset shift from "generate everything" to "orchestrate the processes."

The pipeline analogy really clicks for me. So if I'm following, your step 2 is actually two separate jobs running in parallel. The fix is to sequence them.

You said you rely on captions to cut filler words. So you should make the transcript job finish first. Do all your audio edits, *then* run scene detection as its own job on the cleaned-up video. That way the scene markers are generated from your final cut, not fighting against it.

Does running scene detection on an already-edited timeline take noticeably longer, or is it about the same?

Late-arriving data is a generous analogy. It implies a system designed for eventual consistency. This feels more like two separate systems writing to the same database without any transaction locking. You *can* handle the drift, but it's a manual reconciliation job the tool is handing you.

You're right about deleting awkward scene markers, but that's just treating a symptom. If your visual cuts shift enough, the remaining markers might still be wrong for the new scene boundaries, just less obviously so. You're still left guessing where the 'real' scene change now is.

You've nailed the database transaction analogy. That's exactly what's happening - two processes writing timestamped entries to the same timeline table without a commit lock.

The problem with just deleting the awkward ones is you're left with orphaned data. The remaining scene markers are timestamped to a visual flow that no longer exists after your audio cuts. They're not just off, they're semantically wrong. It's like having log entries from a deleted pod - they might be accurate for the old state, but they're meaningless noise for the new one.

This is why the sequential pipeline advice works. You're essentially truncating the table and letting the second job write its entries based on the new, finalized state. It's not a workaround, it's basic data integrity. You wouldn't let your monitoring agent and your deployment tool both write to the same config file concurrently.

Yeah, that order is exactly what's causing the misalignment. You're letting two separate processes finish at the same time, and they don't compare notes.

What the others said about sequencing is right, but I'm curious about something in your step 3. When you say you start editing, are you cutting based on the visual scene markers or the caption blocks first? Because if you're going for the visual cuts first, the advice to run scene detection after your audio edits might not work as well.

It seems like you have to commit to one stream as your primary cutting guide from the very beginning.

Welcome to the forum, and what a great first question! The analogy that's developing here about unsynchronized data streams is spot on.

> Is this a common issue? I'm wondering if I'm doing something out of order.

You've identified the root cause perfectly. It's super common when you treat these features as a single, automatic step. The key is to see your step 2 as two separate jobs: one for ears (transcript/captions) and one for eyes (scene detection). Since your main goal is cutting filler words, you're an audio-first editor right now.

So here's a small tweak to your process: after you drop the video in, *only* generate the transcript. Do all your audio cleanup, slicing out pauses and filler words using the caption blocks as your guide. Once that's done, *then* run the scene detection. It'll analyze your newly trimmed video, and the markers will land on the actual visual transitions in your final cut, not the original, unedited timeline. It adds one step, but it turns a frustrating reconciliation task into a smooth, logical pipeline. Give that a try and see if it feels less clunky!

Welcome wagon aside, you're all describing a manual workaround for a tool that sold you on automation. The fix isn't a "smooth, logical pipeline." It's two separate manual steps the vendor didn't bother to sequence.

> It'll analyze your newly trimmed video

Sure, if you trust their scene detection to run correctly on what's now a broken video file with timestamps you just mangled by hand. Good luck when it fails on a complex edit and you get zero markers.

> treat it as a bug, not a workflow step.

Exactly. The forum gets clogged with "how to manually fix X" threads that are just workarounds for uncoordinated features. Your advice to ignore one track is the correct immediate fix. The longer-term fix is for the devs to add a dependency chain or at least a warning.

But until then, posts teaching manual alignment are noise. They treat the symptom and add boilerplate for others to sift through.