script using in librispeech and added for research expirementation... not used yet

local/nnet2/run_5a_clean_100.sh

+#!/bin/bash
+
+# This is p-norm neural net training, with the "fast" script, on top of adapted
+# 40-dimensional features.
+
+
+train_stage=-10
+use_gpu=false
+
+. cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+
+if $use_gpu; then
+  if ! cuda-compiled; then
+    cat <<EOF && exit 1 
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA 
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+  fi
+  parallel_opts="--gpu 1"
+  num_threads=1
+  minibatch_size=512
+  dir=exp/nnet5a_clean_100_gpu
+else
+  # with just 4 jobs this might be a little slow.
+  num_threads=4
+  parallel_opts="--num-threads $num_threads" 
+  minibatch_size=128
+  dir=exp/nnet5a_clean_100
+fi
+
+. ./cmd.sh
+. utils/parse_options.sh
+
+if [ ! -f $dir/final.mdl ]; then
+  if [[  $(hostname -f) ==  *.clsp.jhu.edu ]]; then 
+     # spread the egs over various machines.  will help reduce overload of any
+     # one machine.
+     utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/librispeech/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  steps/nnet2/train_pnorm_fast.sh --stage $train_stage \
+   --samples-per-iter 400000 \
+   --parallel-opts "$parallel_opts" \
+   --num-threads "$num_threads" \
+   --minibatch-size "$minibatch_size" \
+   --num-jobs-nnet 4  --mix-up 8000 \
+   --initial-learning-rate 0.01 --final-learning-rate 0.001 \
+   --num-hidden-layers 4 \
+   --pnorm-input-dim 2000 --pnorm-output-dim 400 \
+   --cmd "$decode_cmd" \
+    data/train_clean_100 data/lang exp/tri4b_ali_clean_100 $dir || exit 1
+fi
+
+
+for test in meeting_test; do
+  steps/nnet2/decode.sh --nj 2 --cmd "$decode_cmd" \
+    --transform-dir exp/tri4b/decode_tgsmall_$test \
+    $exp_data/tri3b/graph_tglarge $idata_kaldi/$test $exp_data/nn2/decode_tglarge_$test
+  #steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
+  #  data/$test $dir/decode_{tgsmall,tgmed}_$test  || exit 1;
+  #steps/lmrescore_const_arpa.sh \
+  #  --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
+  #  data/$test $dir/decode_{tgsmall,tglarge}_$test || exit 1;
+  #steps/lmrescore_const_arpa.sh \
+  #  --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
+  #  data/$test $dir/decode_{tgsmall,fglarge}_$test || exit 1;
+done
+
+exit 0;

local/nnet2/run_5c.sh

+#!/bin/bash
+
+# This is neural net training on top of adapted 40-dimensional features.
+# 
+
+train_stage=-10
+use_gpu=true
+
+train_set="train-clean-100"
+test_sets="dev-clean dev-other"
+
+. cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+
+if $use_gpu; then
+  if ! cuda-compiled; then
+    cat <<EOF && exit 1 
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA 
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+  fi
+  parallel_opts="--gpu 1"
+  num_threads=1
+  minibatch_size=512
+  dir=exp/nnet5c_gpu_${train_set}
+else
+  num_threads=16
+  parallel_opts="-pe smp $num_threads" 
+  dir=exp/nnet5c_${train_set}
+  minibatch_size=128
+fi
+
+if [ ! -f $dir/final.mdl ]; then
+  if [ "$USER" == dpovey ]; then
+     # spread the egs over various machines.  will help reduce overload of any
+     # one machine.
+     utils/create_split_dir.pl /export/b0{1,2,3,4}/dpovey/kaldi-pure/egs/wsj/s5/$dir/egs $dir/egs/storage
+  fi
+  steps/nnet2/train_tanh_fast.sh --stage $train_stage \
+    --num-threads "$num_threads" \
+    --parallel-opts "$parallel_opts" \
+    --minibatch-size "$minibatch_size" \
+    --num-jobs-nnet 8 \
+    --samples-per-iter 400000 \
+    --mix-up 8000 \
+    --initial-learning-rate 0.01 --final-learning-rate 0.001 \
+    --num-hidden-layers 4 --hidden-layer-dim 1024 \
+    --cmd "$decode_cmd" \
+     data/$train_set data/lang exp/tri4b_ali_${train_set} $dir || exit 1
+fi
+
+for test in $test_sets; do
+  steps/nnet2/decode.sh --nj 20 --cmd "$decode_cmd" \
+    --transform-dir exp/tri4b/decode_tgpr_$test \
+    exp/tri4b/graph_tgpr data/$test $dir/decode_tgpr_$test || exit 1;
+done
+
+wait
+
+exit 0

local/nnet2/run_6a_clean_460.sh

+#!/bin/bash
+
+# This is p-norm neural net training, with the "fast" script, on top of adapted
+# 40-dimensional features.
+# This version uses 460 hours of "clean" (typically relatively un-accented) 
+# training data.
+# We're using 6 jobs rather than 4, for speed.
+
+# Note: we highly discourage running this with --use-gpu false, it will
+# take way too long.
+
+train_stage=-10
+use_gpu=true
+
+. cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+
+if $use_gpu; then
+  if ! cuda-compiled; then
+    cat <<EOF && exit 1 
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA 
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+  fi
+  parallel_opts="--gpu 1"
+  num_threads=1
+  minibatch_size=512
+  dir=exp/nnet6a_clean_460_gpu
+else
+  # with just 4 jobs this might be a little slow.
+  num_threads=16
+  parallel_opts="-pe smp $num_threads" 
+  minibatch_size=128
+  dir=exp/nnet6a_clean_460
+fi
+
+. ./cmd.sh
+. utils/parse_options.sh
+
+if [ ! -f $dir/final.mdl ]; then
+  if [[  $(hostname -f) ==  *.clsp.jhu.edu ]]; then 
+     # spread the egs over various machines.  will help reduce overload of any
+     # one machine.
+     utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/librispeech/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  steps/nnet2/train_pnorm_fast.sh --stage $train_stage \
+   --samples-per-iter 400000 \
+   --num-epochs 7 --num-epochs-extra 3 \
+   --parallel-opts "$parallel_opts" \
+   --num-threads "$num_threads" \
+   --minibatch-size "$minibatch_size" \
+   --num-jobs-nnet 6  --mix-up 10000 \
+   --initial-learning-rate 0.01 --final-learning-rate 0.001 \
+   --num-hidden-layers 4 \
+   --pnorm-input-dim 4000 --pnorm-output-dim 400 \
+   --cmd "$decode_cmd" \
+    data/train_clean_460 data/lang exp/tri5b $dir || exit 1
+fi
+
+
+for test in test_clean test_other dev_clean dev_other; do
+  steps/nnet2/decode.sh --nj 20 --cmd "$decode_cmd" \
+    --transform-dir exp/tri5b/decode_tgsmall_$test \
+    exp/tri5b/graph_tgsmall data/$test $dir/decode_tgsmall_$test || exit 1;
+  steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
+    data/$test $dir/decode_{tgsmall,tgmed}_$test  || exit 1;
+  steps/lmrescore_const_arpa.sh \
+    --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
+    data/$test $dir/decode_{tgsmall,tglarge}_$test || exit 1;
+  steps/lmrescore_const_arpa.sh \
+    --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
+    data/$test $dir/decode_{tgsmall,fglarge}_$test || exit 1;
+done
+
+exit 0;

local/nnet2/run_7a_960.sh

+#!/bin/bash
+
+# This is p-norm neural net training, with the "fast" script, on top of adapted
+# 40-dimensional features.
+# This version uses 960 hours of mixed (clean + "other") training data.
+# We're using 6 jobs rather than 4, for speed, and 5 hidden layers
+# rather than 4.
+
+# Note: we highly discourage running this with --use-gpu false, it will
+# take way too long.
+
+train_stage=-10
+use_gpu=true
+
+. cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+
+if $use_gpu; then
+  if ! cuda-compiled; then
+    cat <<EOF && exit 1 
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA 
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+  fi
+  parallel_opts="--gpu 1"
+  num_threads=1
+  minibatch_size=512
+  dir=exp/nnet7a_960_gpu
+else
+  # with just 4 jobs this might be a little slow.
+  num_threads=16
+  parallel_opts="-pe smp $num_threads" 
+  minibatch_size=128
+  dir=exp/nnet7a_960
+fi
+
+. ./cmd.sh
+. utils/parse_options.sh
+
+if [ ! -f $dir/final.mdl ]; then
+  if [[  $(hostname -f) ==  *.clsp.jhu.edu ]]; then 
+     # spread the egs over various machines.  will help reduce overload of any
+     # one machine.
+     utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/librispeech/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  steps/nnet2/train_pnorm_fast.sh --stage $train_stage \
+   --samples-per-iter 400000 \
+   --num-epochs 6 --num-epochs-extra 2 \
+   --parallel-opts "$parallel_opts" \
+   --num-threads "$num_threads" \
+   --minibatch-size "$minibatch_size" \
+   --num-jobs-nnet 6  --mix-up 14000 \
+   --initial-learning-rate 0.01 --final-learning-rate 0.001 \
+   --num-hidden-layers 5 \
+   --pnorm-input-dim 5000 --pnorm-output-dim 500 \
+   --cmd "$decode_cmd" \
+    data/train_960 data/lang exp/tri6b $dir || exit 1
+fi
+
+
+for test in test_clean test_other dev_clean dev_other; do
+  steps/nnet2/decode.sh --nj 20 --cmd "$decode_cmd" \
+    --transform-dir exp/tri6b/decode_tgsmall_$test \
+    exp/tri6b/graph_tgsmall data/$test $dir/decode_tgsmall_$test || exit 1;
+  steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
+    data/$test $dir/decode_{tgsmall,tgmed}_$test  || exit 1;
+  steps/lmrescore_const_arpa.sh \
+    --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
+    data/$test $dir/decode_{tgsmall,tglarge}_$test || exit 1;
+  steps/lmrescore_const_arpa.sh \
+    --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
+    data/$test $dir/decode_{tgsmall,fglarge}_$test || exit 1;
+done
+
+exit 0;