Training wav2vac2 requires a lot of compute power  
I am trying to Fine tune wav2vac2 model for my national language and I have 15k data points but while trainings my system could only train on 1k datapoints and if I increase the datapoints my system either crashes or I get CUDA out of memory. so I am wonder is their any other alternatives ??

secondly can I first train on 1k data and then save the model locally and again load the model train with another 1k new data to improve my model ?? will it actually work ?  
Hi @ngawang88, thanks for opening an issue! 

This is a question that is better suited to the [forums](https://discuss.huggingface.co/). We try and keep github issues reserved for bugs and feature requests. <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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