@ -58,6 +58,13 @@ This section will cover how to prepare a dataset for training.
This tab will leverage any voice you have under the `./voices/` folder, and transcribes your voice samples using [openai/whisper](https://github.com/openai/whisper) to prepare an LJSpeech-formatted dataset to train against.
It's not required to dedicate a small portion of your dataset for validation purposes, but it's recommended, as it helps remove data that's too small to be useful for. Using a validation dataset will help measure how well the finetune is at synthesizing speech from an input that it has not trained against.
If you're transcribing English text that's already stored as separate sound files (for example, one sentence per file), there isn't much of a concern with utilizing a larger whisper model, as transcription of English is already very decent with even the smaller models.
However, if you're transcribing something non-Latin (like Japanese), or need your source sliced into segments (if you have everything in one large file), then you should consider using a larger model for better timestamping (however, the large model seems to have some problems providing accurate segmentation).
* **!**NOTE**!**: be very careful with naively trusting how well the audio is segmented. Be sure to manually curate how well
## Generate Configuration
This will generate the YAML necessary to feed into training. For documentation's sake, below are details for what each parameter does:
mrq