run_nnet2.sh
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#!/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