Adding Conformer model
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Here are some of the main changes:
+ Added the modules needed for Conformer
+ Added use_start_end_tokens to the data layers to support dropping these tokens
+ Updated our CTC loss to support different reduction methods including 'mean_batch'. User may select other reduction approaches from Pytorch. User may set it via ctc_reduction param added to the model config.
+ Added log_prediction parameter to model's config to control if we want to see prediction samples in output or not
+ Added LSTM decoder and Swish activation
+ Added NoamScheduler
+ Added subsampling module which supports VGGNet and striding approach for subsampling
+ Added multi-head attention, relative multi-head attention along with positional embedding and relative positional embedding
+ Fixed the bug in the data layer which added some paddings after normalization (fixing this bug would fail the tests! going to investigate it)