#!/bin/bash
  
  # You don't have to run all these.  
  # you can pick and choose.  Look at the RESULTS file..
  
  
  use_gpu=true  
  
  if $use_gpu; then
    # This example runs on top of "raw-fMLLR" features.
    # We don't have a GPU version of this script.
    #local/nnet2/run_4a_gpu.sh
  
    # This one is on top of filter-bank features (VTLN-perturbed), 
    # with only CMN.
    local/nnet2/run_4b_gpu.sh
  
    # This one is on top of 40-dim + fMLLR features.
    local/nnet2/run_4c.sh --use-gpu true
  
    # This one is for training pnorm nnets on top of 40-dim + fMLLR features
    # **THIS IS THE PRIMARY RECIPE**
    local/nnet2/run_4d3.sh --use-gpu true
  
    # this is the old version of the run_4d3.sh script, before
    # switching to more compact version of egs.
    #local/nnet2/run_4d.sh --use-gpu true
  
    # as above with 'perturbed training'.  A bit better results, a bit slower.
    local/nnet2/run_4d2.sh --use-gpu true
    
    # This is discriminative training on top of 4c.  (hardly helps)
    local/nnet2/run_5c_gpu.sh
    
    # This is discriminative training on top of 4d.
    local/nnet2/run_5d.sh --use-gpu true
  else
    # This example runs on top of "raw-fMLLR" features;
    # you have to run local/run_raw_fmllr.sh first.
    local/nnet2/run_4a.sh
  
    # This one is on top of filter-bank features, with only CMN.
    local/nnet2/run_4b.sh
  
    # This one is on top of 40-dim + fMLLR features, it's a fairly
    # normal tanh system.
    local/nnet2/run_4c.sh --use-gpu false
  
    # **THIS IS THE PRIMARY RECIPE (40-dim + fMLLR + p-norm neural net)**
    local/nnet2/run_4d.sh --use-gpu false
  
    # as above with 'perturbed training'.  A bit better results, a bit slower.
    local/nnet2/run_4d2.sh --use-gpu false
  
    # This is discriminative training on top of 4c.
    local/nnet2/run_5c.sh
  
    # This is discriminative training on top of 4d.
    local/nnet2/run_5d.sh --use-gpu false
  
    # This is p-norm on top of raw-fMLLR.
    #local/nnet2/run_4e.sh
  
  fi