Not sure how to train TTS model
Hello!

In this post I would like to ask some questions.
As a premise I want to tell you what kind of machine I am working.

I have got an MSI Creator P100X -637
The specs are 
GPU RTX 3080 
64GB RAM
Intel Core i7-11700K
CPU 3.6 GHz

I work with trial an error and it is very confusing. Depending on the size of the dataset the GPU only accepts a limited max number in batch size.

For example, if I am going to train a dataset of 20 hours it will only accept a max batch size of 6. If I try a greater number it always raises an CUDA out of memory. (I supose it is the most common error when training)

On the other hand, if I am going to train a dataset of 5 hours aprox I can work with a batch size of 14 or 15.

### **Question 1/4.**
Is there a way to estimate the batch size previous to train?

Each time that I start training, if there is too much batch size it will crash. From I start training to crash it can pass like 15-20 minutes. This is because before start training the program needs to load all data previously. So  i wasted a lot of time because until I don't find the good max batch size I can spend hours.

### **Question 2/4.**
Is there a way to estimate how many epochs to train a model based on the size of the dataset and the GPU batch size?

Now I set like 1000 epochs and I let the model training until the training loss is near to 0. But I am sure there is a cleaner way to do it.

### **Question 3/4.**
How to know how much time to train a model?
I supervised the trainings in tensorboard.
I deduced that I should evaluate the training loss of the model to know if is enough trained. But I am not sure of this.

### **Question 4/4.**
How to evaluate when a model is enough trained?

I spend a lots of hours trying to train a model without success. I am sure a lot of people that start with NeMo has this kind of doubts. Also  I read a lot of discussions, issues, etc but I still did not solve this doubts.

So I would apreciate some help. I really want to train a TTS model with my own datasets.

Thank you!