Hi @Rio
Have you looked in the wiki?
These preprocessors are simply a bit of code to get your data (in whatever directory and file format you have) loaded into the training programme.
You don’t mention anything about how your data is currently set up but if you can, you might find it easier to organise your data into the format of an existing preprocessor - the one I use is LJSpeech.
It’s worth looking over the code in dataset/preprocess.py it’s nothing too complicated and should be easy to see what it’s doing. You don’t run it separately, it’s called when your data it loaded and the preprocessor you pick in your config is the one that gets used.
A quick overview of the LJSpeech layout: there’s a folder /wavs for all your wav files (as you’d probably guessed!) And the two CSV files (training and validation) with rows made up of the corresponding filename stem (without the .wav extension) and the text corresponding to the audio. It’s actually a pipe separated file (not CSV really). LJSpeech has the normalised text and the raw text - if you’ve got it already normalised then these can be the same.
If you get stuck or can’t visualise it from this description then perhaps it’s worth downloading LJSpeech so you can see the way it’s organised. As an added bonus, then you’ll be able to do some initial training with LJSpeech data - I’d strongly recommend that over jumping into something you’ve never done before with brand new data, which is a recipe for confusion. Using LJSpeech for an initial run means you’ll get the hang of the basics, flush out human error on your part, and be reasonably sure that you’re not running into issues due to your data, because you’re working with a known good dataset.
I’d suggest having a decent look over the repo too. Generally proof of effort improves the chances of others helping you.
Maybe take a peak in the dev branch too because the README there has been smartened up so you should find links to the info you need quite easily.
Hope that’s a start at least