Had to get our marketing videos accessible fast. Descript's transcription is the fastest path from raw video to compliant captions I've found. Here's the workflow.
* Record your video (screen recording or talking head).
* Drop it into Descript. Let it auto-transcribe. **The key step:** Review the transcript word-for-word while watching. The AI is good, but not perfect—fix names, technical terms.
* Use the subtitle block to adjust caption timing. Break long sentences. Ensure they stay on screen long enough to read.
* Export with "Burn in subtitles" checked. For YouTube/Vimeo, you can also export the SRT file separately.
Gotchas:
* Background music can mess with transcription accuracy. Do a quiet test first.
* If you edit the video by editing the transcript, double-check the captions again. Sometimes the timing gets odd.
* Export settings: Use the highest quality for the burned-in captions. The default can be a bit compressed.
It's not perfect, but it cut our captioning time by about 70%. The transcript editing is the make-or-break part.
// chris
metrics not myths
Your point about the manual review being the critical step is absolutely correct. I'd extend that to say the accuracy requirement depends heavily on the video's purpose.
For internal training videos with technical jargon, we found even a 95% accurate transcript created significant confusion. We had to implement a second review pass specifically for domain-specific terms, using a glossary. The time savings from automated transcription still held, but the review phase became more structured.
For marketing content, the emotional tone and brand name accuracy matter more than pure word-for-word precision. A misplaced "not" in a technical demo is far more damaging than a slightly rephrased marketing sentence. Have you quantified the error rates you're seeing post-review for different video types?
Data never lies.
Seventy percent time savings is a classic vendor claim. Have you actually run the numbers on the total cost, or is that just an estimate of the manual transcription time you avoided?
You're trading one labor cost for another: the hourly rate for someone to do that meticulous word-for-word review. For a one-off video, fine. Scale this to a library of legacy content and that "review phase" becomes a full-time job. Descript's subscription fee plus that labor often equals what a specialized captioning service would charge, and they guarantee 99% accuracy upfront.
What's your error rate after review? If it's still requiring a second pass for technical terms, as the next post suggests, your actual savings are shrinking fast. Speed is only valuable if the output is compliant without hidden rework.
Show me the TCO.
Finally, someone asking about actual cost instead of just speed. I've seen this blow up twice now.
Teams forget to factor in the SME's time for that "quick review." That's not free labor, especially if it's pulling a senior engineer off actual work. The tool's cheap, but the second pass on a 50-video backlog can triple the project's real price.
So yeah, that 70% savings often becomes a 30% net loss once you track all the hours.
-- old school
Exactly. This is a classic capex vs opex trap with automation tools. The initial license cost looks low, but the operational burden shifts to your most expensive people.
We ran into this with our internal knowledge base videos. The real cost wasn't Descript, it was the three senior platform engineers spending half-days verifying Kubernetes command syntax in transcripts. Their loaded rate is far higher than a transcription service's per-minute fee.
The fix was to treat captioning like any other pipeline: define clear SLA tiers. Internal demos get the fast AI pass with a lightweight peer review. Customer-facing content goes straight to a professional service with a technical glossary. You stop the cost bleed by deciding upfront what "good enough" means for each use case.
Thanks for spelling out the exact steps. That's super clear. I'm new to this and was worried about the workflow being complicated.
The part about the transcript editing being the "make-or-break" step is what I'm nervous about. How long does that review usually take you for, say, a 10-minute video? Is it a huge time sink?
Just trying to figure out if my team can realistically handle this in-house.
Your question about the time sink is the right one to ask. For a 10-minute video, a thorough word-for-word review with timing adjustments in Descript typically takes me 30-45 minutes, assuming decent audio quality.
That time can double if the speaker uses heavy jargon, mumbles, or if there's background music. The real trap is thinking this scales linearly. Your first few videos will take longer as you learn the tool, and the cognitive fatigue from reviewing dozens of hours of content is significant and leads to missed errors.
Before committing, run a pilot. Take one 10-minute video and have the intended reviewer track their exact time. Then double it for planning. If that reviewer is a senior engineer costing $150/hour, you've just spent over $100 on a single video. That's when the "in-house" math falls apart for any real volume.
—davidr
70% time savings sounds great, but does that factor in the cost of the reviewer's time? If a senior engineer is pulled off a project to fix technical terms, that's a huge hidden cost.
Has anyone done a real cost comparison between this DIY method and just hiring a pro service that knows your industry terms? Sometimes the cheap tool ends up being the expensive option.
Still learning
Spot on about the purpose dictating the accuracy need. We saw the same split.
For our technical onboarding videos, we actually tracked error rates post-review. The raw AI output had about a 92% accuracy. After the first review pass by a project manager, it went to about 98%. But that remaining 2% was almost exclusively jargon and product names - the exact things that cause confusion. That forced the second, SME-heavy pass you mentioned.
For marketing videos, we care less about verbatim and more about readability and timing. A 95% accurate transcript that flows well with the visuals is often better than a 100% one with awkward pauses.
So our rule became: if a single misunderstood term could break the meaning, it needs the two-pass review. Everything else gets one pass.