text: Title: General question about embedding size
  Body: Hey,
  I am interested in training a one-language model, which also has less accent. I would like to train a good quality model on about 1000 speakers. Then I want to fine tune on a single speaker (like #437) (with 5 minutes or even hours of audio) to finally get a good single speaker model. Now my question is: Does the model benefit from an embedding size (or only hidden size in encoder) of 768 like sberryman did in #126, even if training time and Vram usage increases heavily? Or is it only interesting for multi-language/multi-accent models and I would definitely waste my time with that? Or even get worse results?
  I also use 48000 as sample_rate, as most of my samples (of commonVoice) are in 48k, maybe this has an impact?
  
  Thanks in advance :) 
  