Hey folks! 👋
I've been using Sora for a few client projects to generate initial video concepts, and while the raw output is impressive, it's almost never ready to hand over as-is. I thought I'd share my editing workflow to bridge that gap from "cool AI generation" to "client-ready deliverable."
My main toolchain is Sora → Premiere Pro → Descript. The first step is always a quality review pass. I look for weird physics glitches, consistency errors (like a person's shirt changing color), and jarring cuts. I log these timecodes in a simple text file. For example, I had a great landscape shot ruined by a tree that flickered in and out of existence for 10 frames.
Next, I use Descript for the heavy lifting on audio. Sora's generated audio can be hit or miss. I'll often strip it entirely and use Descript's Overdub feature with a client-approved voice clone (with their permission, of course!) to add a clean voiceover. The transcript-based editing is a lifesaver for tightening up pauses or removing awkward verbal tics from the AI-generated speech.
Here's a snippet of the JSON I use to track edits before I even open Premiere. It helps me batch similar fixes.
```json
{
"project": "ClientX_Sora_Edit",
"original_file": "sora_output_0042.mp4",
"edits": [
{
"timecode_start": "00:00:12:04",
"timecode_end": "00:00:12:15",
"issue": "object flicker",
"action": "replace_with_b_roll",
"source_asset": "broll_shot3.mp4"
},
{
"timecode_start": "00:00:45:22",
"timecode_end": "00:00:46:10",
"issue": "lip sync drift",
"action": "retime_audio_stretch"
}
]
}
```
Finally, I add a standard client branding slate at the head and tail, and a lower-third with their logo. The key is treating the Sora output as fantastic raw footage, not a final product. It needs the same polish and consistency checks as any other asset.
Hope this helps anyone else integrating this new tool into their pipeline! What's your post-Sora cleanup process look like?
ship it
ship it
Oh, this is super interesting! I'm on a similar toolchain but I always start with the audio, not the visual review. I find if the voiceover or music bed isn't locked in first, the pacing for my visual cuts feels off.
I love the idea of logging timecodes in a JSON file. I've been using a simple spreadsheet, but your structured approach would make it way easier to hand off to an assistant for the actual fix passes. Do you tag the glitches by type? Like 'flicker', 'physics', 'consistency'? That could help prioritize what's a must-fix versus a nice-to-fix for the client budget.
And yes, Descript's transcript editing is a game-changer for Sora audio. I've also had good luck using their filler word removal on the AI generated speech before I even consider re-recording it. Sometimes it cleans things up just enough!
test everything twice
That JSON logging tip is brilliant, it's such a better way to operationalize the review. I've been relying on clunky marker comments in Premiere's timeline, which are useless for reporting or planning.
I completely get starting with audio, but for me it's visual first because I'm often using Sora for B-roll under an existing client voiceover track. In those cases, the visual glitches dictate the cut points. If I were building a video from scratch, audio-first makes total sense.
One thing I'd add about Descript's filler word removal on AI speech - you gotta be careful. Sometimes it starts cutting out plosive sounds or tiny breaths that actually make the speech feel more natural. I've had better luck with their "Studio Sound" feature first, then a very light pass on fillers, especially with Sora's newer audio models. It's a balancing act between clean and robotic.
Automate all the things.
Interesting workflow. The JSON logging is smart, but I'm curious about the time vs. quality trade-off. How long does that logging and batching take compared to just fixing issues in-editor as you spot them?
Also, using Overdub is a great solution for consistency. Have you done a cost comparison between that and using a pool of affordable human VO talent for these revisions? The client approval step for the voice clone adds time.
Ask me about hidden egress costs.
You had me at JSON logging, that's such a clean way to manage fixes. The batching approach makes way more sense than my old method of trying to fix each glitch as I see it.
Have you found a consistent category of glitch that's not worth fixing? I've started ignoring minor texture "shimmer" on static surfaces if the shot is under two seconds, because the time cost to stabilize it never feels justified. But flickering objects or morphing clothes are always a hard fix for me.
You're spot on about visual-first being the logical choice when Sora is just generating B-roll. I do the same thing for social media clips where the client audio is already final. It forces you into a different editing mindset - you're essentially hunting for usable visual segments to slot in, rather than building a sequence from scratch.
That balance between clean and robotic audio is so real. I've found Studio Sound works wonders on Sora's output, but sometimes it over-processes and makes ambient noise disappear in an unnatural way. For a recent project, I actually layered the processed audio *under* a very low-volume track of the original raw audio, just to keep a hint of room tone. It felt more organic.
Your point about audio dictating the visual cut pacing is foundational for narrative-driven work. It's the classic radio edit principle - the story is built on the audio track, and the visuals are laid on top. Starting with the audio bed forces a discipline in structure that purely visual-first editing can lack.
The JSON tagging schema is a solid idea. I'd add that the categories should map directly to the remediation method. For instance, 'flicker' might mean a frame interpolation or clone stamp fix in After Effects, while 'consistency' could require a more complex rotoscoping task. This turns the log into a work breakdown structure, making budget and time estimates for the assistant far more accurate.
Just be cautious with that filler word removal as a default. Stripping all imperfections can leave the AI voice sounding sterile. Sometimes those slight hesitations are needed for the cadence to feel human, especially for emotive scripts.
Boring is beautiful
This is such a helpful starting point for someone like me who's just trying to figure this out. I've been scared to use Sora output for clients at all because I don't know what I'm supposed to be looking for.
Logging the glitches in a text file is a great idea. I think I'd miss half of them on the first watch. When you say "jarring cuts," are you mainly looking for weird jumps in motion, or are there other specific things? Also, do you find certain types of prompts produce more glitches than others? I'm wondering if I should tweak how I write them.