Getting Started With AI Captions
Carlyscaptions is one of those caption generation platforms that shows up in every "best of" list but nobody talks about honestly. I spent about six weeks running it against my actual workloads before deciding whether to keep using it or scrap it. Here is what happened. The platform promises automatic caption generation for video content. It claims to analyze your video, understand the context, and produce accurate captions with timestamps. The reality is messier than the marketing copy suggests. You still need to spend time correcting errors, especially when dealing with technical jargon or proper nouns that aren't in the training data. I set it up on a Linux box with Docker. The installation took about twelve minutes. The configuration file is straightforward but the default settings are tuned for English content from North America. If you are working with any other dialect or regional accent, you will need to adjust the confidence thresholds manually. The documentation doesn't mention this anywhere obvious.
Here is the part most tutorials skip. You need to pre-process your audio track before running it through the captioning engine. I found that stripping out background music and isolating the vocal frequencies first improved accuracy by roughly forty percent on my test datasets. The improvement is so significant that I now treat pre-processing as a required step, not an optional optimization. The API returns captions in a few formats: SRT, VTT, and JSON with timestamp objects. For most use cases, SRT is fine. But if you need programmatic access to the timing data for things like synchronized highlight reels or automated chapter markers, the JSON output is actually more useful despite being harder to parse. I wrote a small Node script to convert between the formats because the built-in converters didn't handle edge cases well. Batch processing works but it is slow. Each video goes through the pipeline sequentially unless you pay for the enterprise tier. On the free plan, a five-minute video takes about three minutes to process. Longer videos don't scale linearly. An hour-long recording took roughly twenty-two minutes. That is acceptable for occasional use but painful if you are producing content at any real volume.
The pricing is where things get uncomfortable. The free tier gives you sixty minutes of processing per month. After that, it jumps to fifteen dollars per month for two hundred minutes. If you are generating captions for multiple videos regularly, you will outgrow that quickly. The enterprise tier starts at eighty dollars per month and includes priority processing and custom model fine-tuning. That last feature is interesting if you have domain-specific terminology that the model keeps getting wrong. I ran into a specific problem with names. The platform kept mishearing "Carlyscaptions" as "Carly's captions" or sometimes just "carls capions." This happened consistently across multiple test videos. The workaround was to add the correct spelling to the custom vocabulary file. Once I did that, the accuracy for that particular term jumped from about sixty percent to nearly ninety-five percent. But you have to be careful not to overdo it. Loading too many custom terms into the vocabulary can degrade performance on unrelated words. Another issue I encountered involves multilingual content. The platform claims to support twenty-three languages but the quality varies wildly. English, Spanish, and Mandarin were decent. Everything else was hit or miss. I tried German once and the timestamps were mostly correct but the actual text was full of grammatical errors that made it unusable without heavy manual correction. If your audience is primarily non-English speaking, test thoroughly before committing to this as your primary solution.
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The export options are limited compared to some competitors. You can download in standard formats but there is no direct integration with major video editing software like Adobe Premiere or DaVinci Resolve. You end up importing the SRT file manually or writing a script to inject it. This is a minor inconvenience but it adds up if you are doing this daily. Customer support is basically nonexistent on the free tier. I submitted two tickets during my testing period. The first one got a generic response within forty-eight hours. The second ticket about a timeout error never received a reply. On the paid plans you get email priority support but I have not heard whether that is actually better. Nobody seems to use the enterprise tier enough to give honest feedback. The platform does have some genuine strengths. The automated punctuation is better than most alternatives I have tested. Many caption generators output streams of lowercase text with no periods or commas. Carlyscaptions at least attempts to structure sentences properly, though the results are inconsistent. Proper nouns and acronyms are where it struggles most. Technical abbreviations like "API" or "JSON" are often lowercased or split incorrectly.
If you are looking for alternatives, Whisper from OpenAI is worth considering for basic transcription needs. It is free, runs locally, and handles most languages better than Carlyscaptions. The downside is that it does not produce formatted caption files with timestamps out of the box. You would need to pipe the output through something like Whisper.cpp or a Python library to get usable results. The setup is more involved but the accuracy is generally superior, especially for non-native English speakers. For people who need captions at scale and have budget for it, Rev.com is the traditional option. Human-generated captions cost about twenty-five dollars per hour of video but the accuracy is near perfect. If your content requires legal compliance or accessibility certification, this is probably the safer choice despite the cost. I ended up keeping Carlyscaptions in my workflow for a specific reason. It handles short-form social media content reasonably well when pre-processed correctly. The automated timing is good enough for Instagram Reels and TikTok where viewers watch at normal speed and do not read every word carefully. For long-form educational content or documentary work, I switched to a hybrid approach. I run the video through Whisper first, then use Carlyscaptions only for the formatting and timing adjustments. This cuts my total processing time down to about eight minutes per five-minute video while maintaining higher accuracy.
The platform gets updated frequently. I noticed new features appearing every month, including speaker diarization and sentiment tagging. These are nice to have but the core captioning engine has not fundamentally changed since launch. If the company continues investing in accuracy improvements rather than feature bloat, it might become more competitive in a year or two. Right now it occupies a middle ground that is neither the cheapest nor the best. Download the platform from their official website. Third-party mirrors exist but carrying them is risky since the software runs on your machine with access to your content. I have not encountered any malware in the official release but the open source community has flagged a couple of third-party forks for suspicious network behavior. Stick to the main distribution channel. The learning curve is shallow for basic usage. You can be generating captions within thirty minutes of installation if your content is simple. Understanding all the configuration options and edge case handling takes longer. I recommend starting with the default settings, reviewing the output carefully, and only adjusting parameters when you see consistent errors. The platform will not improve automatically just because you tweak settings you do not fully understand.
