OraFrom sound waveform to written tokens
A microphone produces a sequence of audio samples. The recognizer converts short sections of that signal into a representation of frequency over time, then predicts written tokens that best match the spoken content and language context.
This is why background noise, overlapping speakers, microphone distance and unusual names can change the result. The model is not listening like a person. It is estimating the most likely text from the available signal.
Model size and hardware
Larger models generally need more memory and computing time. A smaller model can feel more responsive on a CPU, while a compatible GPU can process a larger model efficiently. Good software should choose a sensible default for the available hardware instead of always loading the biggest model.
Local processing
When the model and runtime are installed on the computer, audio can be processed without uploading it to a transcription service. Local mode improves privacy and can work offline after setup, but performance depends on the user's CPU, GPU and memory.
Why accuracy is not one universal percentage
Recognition quality varies with language, accent, vocabulary, recording quality and evaluation dataset. A percentage reported by one product cannot be assumed for every speaker or document. A practical trial with representative material is more useful than a universal marketing claim.
Real-time dictation and file transcription
The same recognition family can process short microphone recordings or long audio files. Interactive dictation prioritizes response time after the user releases the shortcut. File transcription can process longer segments and produce subtitle timestamps or document exports.
What the user should always review
Names, numbers, dates, medication, measurements and negative statements deserve particular attention. Custom corrections can improve recurring terminology, but final review remains essential for consequential documents.