Preparing text for voiceover: rhythm, pauses, stress
This assistant turns written text into material that is easy for a voice actor to read aloud or to feed into speech synthesis, taking care of the text preparation for voiceover, which usually requires a lot of manual editing. It will break long sentences into short phrases with a natural rhythm, write out numbers, dates, abbreviations, and acronyms in words, suggest stress in difficult words and names, set pauses and logical accents, remove difficult-to-pronounce sound combinations, and adjust the text to the required timing of a video, podcast, or video lesson. On BotHub, you can use the assistant without a VPN or a foreign card, and pay in rubles from a single balance. Chat history is saved, so it's easy to return to previous versions of the script and refine them.
Frequently Asked Questions
Yes. The assistant takes into account that a synthesizer reads text literally. Therefore, it replaces numbers, symbols, units of measurement, and abbreviations with words, simplifies syntax, and can mark stress with a '+' sign or a capitalized vowel. Specify the format supported by your voiceover software in your request, and it will prepare the text accordingly.
Specify the duration of the video and the speech tempo in your request, for example, calm for an educational video or fast for an advertisement. The assistant will estimate the volume of the text and shorten secondary details or, conversely, expand on the idea while preserving the main meaning. The exact sounding time should be checked with a test reading because it depends on the speaker's manner.
In common words, stress is usually placed correctly, but errors are possible in rare surnames, toponyms, terms, and words with variable pronunciation. It is better to check such places with a dictionary or clarify with the client. In addition, it is worth reading the finished text aloud in its entirety: this way, awkward sound combinations and rhythm failures are immediately audible.
CAPS are deducted from the single balance for each request, and the consumption depends primarily on the volume: the longer the source text and the assistant's response, the higher the deduction. It is convenient to process long scripts in parts. This makes it easier to check the result and edit individual fragments without rewriting the entire text from scratch.
In a regular chat, you have to explain the rules for preparing text for voiceover every time, and the neural network is often limited to literary editing. Here, these requirements are already set. The assistant immediately thinks about how the text will sound, not how it looks on the screen, so without extra instructions, it writes out numbers in words, monitors phrase length, pauses, and euphony.