[scripts,egs] simplify nnet3 scripts by removing unused feature types…

… (LDA, delta); add sMBR recipe for mini-librispeech (#1711)

egs/librispeech/s5/local/chain/run_tdnn_discriminative.sh

@@ -184,7 +184,6 @@ if [ $stage -le 4 ]; then
     --num-epochs $num_epochs --one-silence-class $one_silence_class --minibatch-size $minibatch_size \
     --num-jobs-nnet $num_jobs_nnet --num-threads $num_threads \
     --regularization-opts "$regularization_opts" --use-frame-shift false \
-    --adjust-priors false \
       ${degs_dir} $dir ;
 fi
 

egs/mini_librispeech/s5/conf/decode.config

@@ -0,0 +1 @@
+# empty config, just use the defaults.

egs/mini_librispeech/s5/local/chain/run_tdnn_discriminative.sh

@@ -0,0 +1 @@
+tuning/run_tdnn_1c_discriminative.sh

egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1c_discriminative.sh

@@ -0,0 +1,250 @@
+#!/bin/bash
+
+set -o pipefail
+set -e
+# this is run_discriminative.sh
+
+# This script does discriminative training on top of chain nnet3 system.
+# note: this relies on having a cluster that has plenty of CPUs as well as GPUs,
+# the lattice generation runs in about real-time
+
+stage=0
+train_stage=-10 # can be used to start training in the middle.
+get_egs_stage=-10
+use_gpu=true  # for training
+cleanup=false  # run with --cleanup true --stage 6 to clean up (remove large things like denlats,
+               # alignments and degs).
+
+. cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+srcdir=exp/chain/tdnn1c_sp
+train_data_dir=data/train_clean_5_sp_hires
+online_ivector_dir=exp/nnet3/ivectors_train_clean_5_sp_hires
+tree_dir=exp/chain/tree_sp
+degs_dir=                     # If provided, will skip the degs directory creation
+lats_dir=                     # If provided, will skip denlats creation
+
+## Objective options
+criterion=smbr
+one_silence_class=true
+
+dir=${srcdir}_${criterion}
+
+## Egs options
+frames_per_eg=150
+frames_overlap_per_eg=30
+
+## Nnet training options
+effective_learning_rate=0.00001
+max_param_change=1
+num_jobs_nnet=4
+num_epochs=4
+regularization_opts="--xent-regularize=0.1 --l2-regularize=0.00005"          # Applicable for providing --xent-regularize and --l2-regularize options
+minibatch_size=64
+
+## Decode options
+decode_start_epoch=1 # can be used to avoid decoding all epochs, e.g. if we decided to run more.
+
+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.  Otherwise, call this script with --use-gpu false
+EOF
+  fi
+  num_threads=1
+else
+  # Use 4 nnet jobs just like run_4d_gpu.sh so the results should be
+  # almost the same, but this may be a little bit slow.
+  num_threads=16
+fi
+
+if [ ! -f ${srcdir}/final.mdl ]; then
+  echo "$0: expected ${srcdir}/final.mdl to exist; first run run_tdnn.sh or run_lstm.sh"
+  exit 1;
+fi
+
+lang=data/lang
+
+frame_subsampling_opt=
+frame_subsampling_factor=1
+if [ -f $srcdir/frame_subsampling_factor ]; then
+  frame_subsampling_factor=$(cat $srcdir/frame_subsampling_factor)
+  frame_subsampling_opt="--frame-subsampling-factor $(cat $srcdir/frame_subsampling_factor)"
+fi
+
+affix=    # Will be set if doing input frame shift
+if [ $frame_subsampling_factor -ne 1 ]; then
+  if [ $stage -le 0 ]; then
+    mkdir -p ${online_ivector_dir}_fs
+    cp -r $online_ivector_dir/{conf,ivector_period} ${online_ivector_dir}_fs
+
+    rm ${online_ivector_dir}_fs/ivector_online.scp 2>/dev/null || true
+
+    data_dirs=
+    for x in `seq -$[frame_subsampling_factor/2] $[frame_subsampling_factor/2]`; do
+      steps/shift_feats.sh --cmd "$train_cmd --max-jobs-run 40" --nj 30 \
+        $x $train_data_dir exp/shift_hires/ mfcc_hires
+      utils/fix_data_dir.sh ${train_data_dir}_fs$x
+      data_dirs="$data_dirs ${train_data_dir}_fs$x"
+      awk -v nfs=$x '{print "fs"nfs"-"$0}' $online_ivector_dir/ivector_online.scp >> ${online_ivector_dir}_fs/ivector_online.scp
+    done
+    utils/combine_data.sh ${train_data_dir}_fs $data_dirs
+    for x in `seq -$[frame_subsampling_factor/2] $[frame_subsampling_factor/2]`; do
+      rm -r ${train_data_dir}_fs$x
+    done
+  fi
+
+  train_data_dir=${train_data_dir}_fs
+
+  affix=_fs
+fi
+
+rm ${online_ivector_dir}_fs/ivector_online.scp 2>/dev/null || true
+for x in `seq -$[frame_subsampling_factor/2] $[frame_subsampling_factor/2]`; do
+  awk -v nfs=$x '{print "fs"nfs"-"$0}' $online_ivector_dir/ivector_online.scp >> ${online_ivector_dir}_fs/ivector_online.scp
+done
+online_ivector_dir=${online_ivector_dir}_fs
+
+if [ $stage -le 1 ]; then
+  # hardcode no-GPU for alignment, although you could use GPU [you wouldn't
+  # get excellent GPU utilization though.]
+  nj=350 # have a high number of jobs because this could take a while, and we might
+         # have some stragglers.
+  steps/nnet3/align.sh  --cmd "$decode_cmd" --use-gpu false \
+    --online-ivector-dir $online_ivector_dir \
+    --scale-opts "--transition-scale=1.0 --acoustic-scale=1.0 --self-loop-scale=1.0" \
+    --nj $nj $train_data_dir $lang $srcdir ${srcdir}_ali${affix} ;
+fi
+
+if [ -z "$lats_dir" ]; then
+  lats_dir=${srcdir}_denlats${affix}
+  if [ $stage -le 2 ]; then
+    nj=50
+    # this doesn't really affect anything strongly, except the num-jobs for one of
+    # the phases of get_egs_discriminative.sh below.
+    num_threads_denlats=6
+    subsplit=40 # number of jobs that run per job (but 2 run at a time, so total jobs is 80, giving
+    # total slots = 80 * 6 = 480.
+    steps/nnet3/make_denlats.sh --cmd "$decode_cmd" \
+      --self-loop-scale 1.0 --acwt 1.0 --determinize true \
+      --online-ivector-dir $online_ivector_dir \
+      --nj $nj --sub-split $subsplit --num-threads "$num_threads_denlats" --config conf/decode.config \
+      $train_data_dir $lang $srcdir ${lats_dir} ;
+  fi
+fi
+
+model_left_context=`nnet3-am-info $srcdir/final.mdl | grep "left-context:" | awk '{print $2}'`
+model_right_context=`nnet3-am-info $srcdir/final.mdl | grep "right-context:" | awk '{print $2}'`
+
+left_context=$[model_left_context + extra_left_context]
+right_context=$[model_right_context + extra_right_context]
+
+cmvn_opts=`cat $srcdir/cmvn_opts`
+
+if [ -z "$degs_dir" ]; then
+  degs_dir=${srcdir}_degs${affix}
+
+  if [ $stage -le 3 ]; then
+    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d ${srcdir}_degs/storage ]; then
+      utils/create_split_dir.pl \
+        /export/b0{3,4,5,6}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5/${srcdir}_degs/storage ${srcdir}_degs/storage
+    fi
+    # have a higher maximum num-jobs if
+    if [ -d ${srcdir}_degs/storage ]; then max_jobs=10; else max_jobs=5; fi
+
+    steps/nnet3/get_egs_discriminative.sh \
+      --cmd "$decode_cmd --max-jobs-run $max_jobs --mem 20G" --stage $get_egs_stage --cmvn-opts "$cmvn_opts" \
+      --adjust-priors false --acwt 1.0 \
+      --online-ivector-dir $online_ivector_dir \
+      --left-context $left_context --right-context $right_context \
+      $frame_subsampling_opt \
+      --frames-per-eg $frames_per_eg --frames-overlap-per-eg $frames_overlap_per_eg \
+      $train_data_dir $lang ${srcdir}_ali${affix} $lats_dir $srcdir/final.mdl $degs_dir ;
+  fi
+fi
+
+if [ $stage -le 4 ]; then
+  steps/nnet3/train_discriminative.sh --cmd "$decode_cmd" \
+    --stage $train_stage \
+    --effective-lrate $effective_learning_rate --max-param-change $max_param_change \
+    --criterion $criterion --drop-frames true --acoustic-scale 1.0 \
+    --num-epochs $num_epochs --one-silence-class $one_silence_class --minibatch-size $minibatch_size \
+    --num-jobs-nnet $num_jobs_nnet --num-threads $num_threads \
+    --regularization-opts "$regularization_opts" --use-frame-shift false \
+    ${degs_dir} $dir ;
+fi
+
+# decode the adjusted model
+if [ $stage -le 5 ]; then
+  rm $dir/.error 2>/dev/null || true
+
+  for x in `seq $decode_start_epoch $num_epochs`; do
+    for data in dev_clean_2; do
+        (
+        iter=epoch${x}_adj
+        nspk=$(wc -l <data/${data}_hires/spk2utt)
+
+        steps/nnet3/decode.sh \
+          --iter $iter \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --extra-left-context 0 \
+          --extra-right-context 0 \
+          --extra-left-context-initial 0 \
+          --extra-right-context-final 0 \
+          --frames-per-chunk 140 \
+          --nj $nspk --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3/ivectors_${data}_hires \
+          $tree_dir/graph_tgsmall data/${data}_hires ${dir}/decode_tgsmall_${data}_${iter} || exit 1
+        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
+            data/lang_test_{tgsmall,tglarge} \
+            data/${data}_hires ${dir}/decode_{tgsmall,tglarge}_${data}_${iter} || exit 1
+        ) || touch $dir/.error &
+    done
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# decode the normal model
+if [ $stage -le 6 ]; then
+  rm $dir/.error 2>/dev/null || true
+
+  for x in `seq $decode_start_epoch $num_epochs`; do
+    for data in dev_clean_2; do
+        (
+        iter=epoch${x}
+        nspk=$(wc -l <data/${data}_hires/spk2utt)
+
+        steps/nnet3/decode.sh \
+          --iter $iter \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --extra-left-context 0 \
+          --extra-right-context 0 \
+          --extra-left-context-initial 0 \
+          --extra-right-context-final 0 \
+          --frames-per-chunk 140 \
+          --nj $nspk --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3/ivectors_${data}_hires \
+          $tree_dir/graph_tgsmall data/${data}_hires ${dir}/decode_tgsmall_${data}_${iter} || exit 1
+        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
+            data/lang_test_{tgsmall,tglarge} \
+            data/${data}_hires ${dir}/decode_{tgsmall,tglarge}_${data}_${iter} || exit 1
+        ) || touch $dir/.error &
+    done
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+if [ $stage -le 7 ] && $cleanup; then
+  # if you run with "--cleanup true --stage 6" you can clean up.
+  rm ${lats_dir}/lat.*.gz || true
+  rm ${srcdir}_ali/ali.*.gz || true
+  steps/nnet2/remove_egs.sh ${srcdir}_degs || true
+fi
+
+exit 0;

egs/swbd/s5c/local/chain/tuning/run_blstm_6h_discriminative.sh

@@ -191,8 +191,6 @@ if [ $stage -le 4 ]; then
     --num-epochs $num_epochs --one-silence-class $one_silence_class --minibatch-size $minibatch_size \
     --num-jobs-nnet $num_jobs_nnet --num-threads $num_threads \
     --regularization-opts "$regularization_opts" --use-frame-shift false \
-    --adjust-priors false \
-    --modify-learning-rates false \
       ${degs_dir} $dir ;
 fi
 
@@ -202,7 +200,7 @@ if [ $stage -le 5 ]; then
     for decode_set in train_dev eval2000 rt03; do
       (
       num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
-      iter=epoch$x.adj
+      iter=epoch$x_adj
 
       steps/nnet3/decode.sh --nj $num_jobs --cmd "$decode_cmd" --iter $iter \
         --acwt 1.0 --post-decode-acwt 10.0 \

egs/swbd/s5c/local/chain/tuning/run_tdnn_6h_discriminative.sh

@@ -218,8 +218,6 @@ if [ $stage -le 4 ]; then
     --num-epochs $num_epochs --one-silence-class $one_silence_class --minibatch-size $minibatch_size \
     --num-jobs-nnet $num_jobs_nnet --num-threads $num_threads \
     --regularization-opts "$regularization_opts" --use-frame-shift false \
-    --adjust-priors false \
-    --modify-learning-rates false \
       ${degs_dir} $dir ;
 fi
 
@@ -229,7 +227,7 @@ if [ $stage -le 5 ]; then
     for decode_set in train_dev eval2000 rt03; do
       (
       num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
-      iter=epoch$x.adj
+      iter=epoch$x_adj
 
       steps/nnet3/decode.sh --nj $num_jobs --cmd "$decode_cmd" --iter $iter \
         --acwt 1.0 --post-decode-acwt 10.0 \

egs/tedlium/s5/local/nnet3/run_tdnn_discriminative.sh

@@ -159,7 +159,7 @@ if [ $stage -le 5 ]; then
     for decode_set in dev test; do
       (
       num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
-      iter=epoch$x.adj
+      iter=epoch${x}_adj
 
       steps/nnet3/decode.sh --nj $num_jobs --cmd "$decode_cmd" --iter $iter \
         --online-ivector-dir exp/nnet3/ivectors_${decode_set} \

egs/wsj/s5/local/nnet3/run_lstm_discriminative.sh

@@ -163,7 +163,7 @@ fi
 
 if [ $stage -le 5 ]; then
   for x in `seq $decode_start_epoch $num_epochs`; do
-    iter=epoch$x.adj
+    iter=epoch${x}_adj
     for lm_suffix in tgpr bd_tgpr; do
       graph_dir=exp/tri4b/graph_${lm_suffix}
       # use already-built graphs.

egs/wsj/s5/local/nnet3/run_tdnn_discriminative.sh

@@ -144,7 +144,7 @@ fi
 
 if [ $stage -le 5 ]; then
   for x in `seq $decode_start_epoch $num_epochs`; do
-    iter=epoch$x.adj
+    iter=epoch${x}_adj
     for lm_suffix in tgpr bd_tgpr; do
       graph_dir=exp/tri4b/graph_${lm_suffix}
       # use already-built graphs.

egs/wsj/s5/steps/libs/nnet3/train/chain_objf/acoustic_model.py

@@ -67,7 +67,7 @@ def generate_chain_egs(dir, data, lat_dir, egs_dir,
                        left_context_initial=-1, right_context_final=-1,
                        frame_subsampling_factor=3,
                        alignment_subsampling_factor=3,
-                       feat_type='raw', online_ivector_dir=None,
+                       online_ivector_dir=None,
                        frames_per_iter=20000, frames_per_eg_str="20", srand=0,
                        egs_opts=None, cmvn_opts=None, transform_dir=None):
     """Wrapper for steps/nnet3/chain/get_egs.sh
@@ -79,7 +79,6 @@ def generate_chain_egs(dir, data, lat_dir, egs_dir,
         """steps/nnet3/chain/get_egs.sh {egs_opts} \
                 --cmd "{command}" \
                 --cmvn-opts "{cmvn_opts}" \
-                --feat-type {feat_type} \
                 --transform-dir "{transform_dir}" \
                 --online-ivector-dir "{ivector_dir}" \
                 --left-context {left_context} \
@@ -97,7 +96,6 @@ def generate_chain_egs(dir, data, lat_dir, egs_dir,
                 {data} {dir} {lat_dir} {egs_dir}""".format(
                     command=run_opts.command,
                     cmvn_opts=cmvn_opts if cmvn_opts is not None else '',
-                    feat_type=feat_type,
                     transform_dir=(transform_dir
                                    if transform_dir is not None
                                    else ''),

egs/wsj/s5/steps/libs/nnet3/train/frame_level_objf/acoustic_model.py

@@ -22,7 +22,7 @@ def generate_egs(data, alidir, egs_dir,
                  left_context, right_context,
                  run_opts, stage=0,
                  left_context_initial=-1, right_context_final=-1,
-                 feat_type='raw', online_ivector_dir=None,
+                 online_ivector_dir=None,
                  samples_per_iter=20000, frames_per_eg_str="20", srand=0,
                  egs_opts=None, cmvn_opts=None, transform_dir=None):
 
@@ -36,7 +36,6 @@ def generate_egs(data, alidir, egs_dir,
         """steps/nnet3/get_egs.sh {egs_opts} \
                 --cmd "{command}" \
                 --cmvn-opts "{cmvn_opts}" \
-                --feat-type {feat_type} \
                 --transform-dir "{transform_dir}" \
                 --online-ivector-dir "{ivector_dir}" \
                 --left-context {left_context} \
@@ -50,7 +49,6 @@ def generate_egs(data, alidir, egs_dir,
                 {data} {alidir} {egs_dir}
         """.format(command=run_opts.command,
                    cmvn_opts=cmvn_opts if cmvn_opts is not None else '',
-                   feat_type=feat_type,
                    transform_dir=(transform_dir
                                   if transform_dir is not None else
                                   ''),