Please note that not all models have these features.
"Post-processing" switch: If enabled (i.e., the switch is green), this will automatically add punctuation (,.!?) and capitalization (the first letter of a sentence will be written with a capital letter) to your transcript. It will add entity recognition (i.e., brand names, person names, national holidays, etc., written with a capital letter). It will also add a numerals conversion layer model, which will try, based on context, to rewrite numbers from letters to digits (e.g., thirteen will become 13, or two will become 2). It will also disable the disfluencies (e.g. grunts or non-lexical utterances such as "huh", "uh", "erm", "um", "hmm", etc.).
"Speakers Diarization" switch: If enabled (i.e., the switch is green), a speaker recognition model will be added to your transcript. This means that the paragraphs of the resulting transcript will be split based on which speaker is speaking at a given time.
"Multiple Channels" switch: If enabled (i.e., the switch is green), a speech recognition model based on the channel will be added to your transcript. This means that the paragraphs of the resulting transcript will be split based on the channels of that file. This is very useful for files that come from call centers, as these always have two channels - the client channel and the agent channel. Please note that you may use only one of the "Speakers Diarization" or "Multiple Channels" switches.
"Add words to your custom vocabulary" input: This is useful when you want to tell our models to watch out for some words, and if it finds them in your transcript, to keep them as they are (e.g., both "ate" and "eight" words sound the same. By writing "ate" in this input, you will tell our model that when it hears either "ate" or "eight," you want to keep "ate"). Note you can add multiple words.
"Select a boost param for these words" selector/input: This is tied up with the previous option. The higher this number is, the higher the chance that the word you want will be kept (e.g., once again, we will use the "ate" and "eight" words. Our model, when checks the sound of a word, it gives accuracy. If that accuracy is lower than your boost param, then the word you added to the above input will be chosen. For example, our model gives "ate" an accuracy of 4.73 and "eight" an accuracy of 8.31. You add the word "ate" in the above input, and you give it a boost of 9. In this case, the model will choose the word "ate" with an accuracy of 4.73, over the word "eight" with an accuracy of 8.31. Suppose you added the bost to 6, then the model would have chosen the word "eight"). Note that all words will have the same boost.
"Save as default configuration" checkbox: If you check this box, then everything in the upload modal pop-up (from language to boost param) will be kept for you next time you upload a new file, so you won't have to switch again, add custom vocabulary, etc.