A few years back, a 200,000-subscriber Korean culinary channel made the decision to attempt translating a recipe video into Spanish. The author was not fluent in Spanish. After using a portal to engage a freelance translator, she waited five days, received a file with timing mistakes, paid $80, corrected the errors herself, and uploaded the movie three weeks after it was shot. In its first month, the Spanish version had more views than the Korean original, which received only six.
After that, she performed the math on the sixty videos that were waiting to be translated on her hard drive. It would take three years and many thousand dollars to catch up at that rate. She put the project on hold. Anyone who has worked in the independent creative economy is likely familiar with this narrative in one form or another: the disparity between the audience you can truly reach and the audience that already exists, as measured by time and translation expenses.
Among the various AI video solutions attempting to bridge that gap, HitPaw Edimakor’s bilingual subtitle engine is the most noteworthy feature. Without human intervention at the individual caption level, the platform automatically transcribes audio, creates captions in the original language, translates them into a target language, and syncs both versions to display simultaneously on the screen.
The system, which supports more than 120 languages, including Spanish, French, Hindi, and Chinese, is designed to mimic the workflow that solo creators actually use: import the video, select your language pair, let the AI run, review and make any necessary adjustments, then export. When compared to a five-day turnaround and a freelancing billing, the three-step approach is really that simple.
The product becomes more intriguing and, it should be noted, more difficult to assess when it comes to the AI dubbing component. In addition to creating subtitles, Edimakor’s Text-to-Speech engine can automatically modify the speech rate to fit the rhythm and pacing of the original video by cloning the original audio track and producing a dubbed version in the target language.
Technically, this is amazing. The question of whether the output sounds natural enough for audiences who are native speakers of the target language is a distinct one, and it likely varies significantly according on the audience’s tolerance, the complexity of the original speech, and the language pair. Although dubbed AI voices have significantly improved over the past two years, keen listeners can still detect a certain quality in them.
The simultaneous bilingual display function is what Edimakor does well and sets it apart from more general translation tools. A particular and underutilized use case—language learners, bilingual households, and audiences who are at ease in one language but need access to the other—is serviced by simultaneously displaying the original language and the translated caption on screen.
By 2027, the global video localization market is expected to surpass $3 billion, driven in part by streaming demand and in part by the creator economy’s slow recognition that the audience for any given piece of content is much bigger than the audience that speaks the artist’s original tongue.
It seems like the tools are advancing more quickly than the workflows surrounding them as this area develops. The best languages to prioritize, how to display bilingual content without overcrowding the screen, and whether AI dubbing increases or decreases credibility with particular audiences are still issues that creators are trying to figure out. Not all of those questions are answered by Edimakor.
However, compared to three years ago, the Korean cookery creator’s math appears significantly different. Three languages, sixty videos, and no freelance queue. Sustained use will determine whether the quality maintains at that level over time.
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