chain branch: various changes to get decoding working; various mostly…

… minor fixes; change threshold in randomized pruning for beta.

egs/swbd/s5c/local/chain/run_tdnn_a.sh

@@ -119,7 +119,8 @@ fi
 
 if [ $stage -le 13 ]; then
   # Note: it might appear that this $lang directory is mismatched, and it is as
-  # far as the 'topo'
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
   utils/mkgraph.sh --transition-scale 0.0 \
       --self-loop-scale 0.0 data/lang_sw1_tg $dir $dir/graph_sw1_tg
 fi
@@ -129,7 +130,7 @@ graph_dir=$dir/graph_sw1_tg
 if [ $stage -le 14 ]; then
   for decode_set in train_dev eval2000; do
       (
-      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 --iter 298_cached \
+      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
           --nj 50 --cmd "$decode_cmd" \
           --online-ivector-dir exp/nnet3/ivectors_${decode_set} \
          $graph_dir data/${decode_set}_hires $dir/decode_${decode_set}_${decode_suff} || exit 1;
@@ -138,7 +139,7 @@ if [ $stage -le 14 ]; then
             data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \
             $dir/decode_${decode_set}_sw1_{tg,fsh_fg} || exit 1;
       fi
-      ) # &
+      ) &
   done
 fi
 wait;

egs/swbd/s5c/local/score_sclite.sh

@@ -51,18 +51,16 @@ nnet3-ctc-info --print-args=false $model  1>/dev/null 2>&1;
 [ $? -eq 0 ] && is_ctc=true;
 [ -z $is_ctc ] && echo "Unknown model type, verify if $model exists" && exit -1;
 align_word=
-reorder=
+reorder_opt=
 if $reverse; then
   align_word="lattice-reverse ark:- ark:- |"
-  reorder="--reorder=false"
+  reorder_opt="--reorder=false"
 fi
 
 if $is_ctc ; then
-  echo "Warning : This is a CTC model, using corresponding scoring pipeline."
+  echo "Warning : This is a 'chain' model, using corresponding scoring pipeline."
   factor=$(cat $dir/../frame_subsampling_factor) || exit 1
   frame_shift_opt="--frame-shift=0.0$factor"
-else
-  align_word="$align_word lattice-align-words $reorder $lang/phones/word_boundary.int $model ark:- ark:- |"
 fi
 
 name=`basename $data`; # e.g. eval2000
@@ -75,7 +73,8 @@ if [ $stage -le 0 ]; then
       mkdir -p $dir/score_LMWT_${wip}/ '&&' \
       lattice-scale --lm-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
       lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \
-      lattice-1best ark:- ark:- \| $align_word \
+      lattice-1best ark:- ark:- \| \
+      lattice-align-words $reorder_opt $lang/phones/word_boundary.int $model ark:- ark:- \| \
       nbest-to-ctm $frame_shift_opt ark:- - \| \
       utils/int2sym.pl -f 5 $lang/words.txt  \| \
       utils/convert_ctm.pl $data/segments $data/reco2file_and_channel \

egs/wsj/s5/steps/nnet3/chain/train_tdnn.sh

@@ -182,7 +182,7 @@ if [ $stage -le -6 ]; then
   echo "$0: creating denominator FST"
   copy-transition-model $treedir/final.mdl $dir/0.trans_mdl
   $cmd $dir/log/make_den_fst.log \
-    chain-make-den-graph $dir/tree $dir/0.trans_mdl $dir/phone_lm.fst \
+    chain-make-den-fst $dir/tree $dir/0.trans_mdl $dir/phone_lm.fst \
        $dir/den.fst $dir/normalization.fst || exit 1;
 fi
 

egs/wsj/s5/steps/nnet3/decode.sh

@@ -128,7 +128,7 @@ fi
 
 if [ ! -z "$online_ivector_dir" ]; then
   ivector_period=$(cat $online_ivector_dir/ivector_period) || exit 1;
-  ivector_opts="--online-ivectors=scp:$online_ivector_dir/ivector_online.scp --online-ivector_period=$ivector_period"
+  ivector_opts="--online-ivectors=scp:$online_ivector_dir/ivector_online.scp --online-ivector-period=$ivector_period"
 fi
 
 if [ "$post_decode_acwt" == 1.0 ]; then
@@ -137,9 +137,14 @@ else
   lat_wspecifier="ark:|lattice-scale --acoustic-scale=$post_decode_acwt ark:- ark:- | gzip -c >$dir/lat.JOB.gz"
 fi
 
+if [ -f $srcdir/frame_subsampling_factor ]; then
+  # e.g. for 'chain' systems
+  frame_subsampling_opt="--frame-subsampling-factor=$(cat $srcdir/frame_subsampling_factor)"
+fi
+
 if [ $stage -le 1 ]; then
   $cmd --num-threads $num_threads JOB=1:$nj $dir/log/decode.JOB.log \
-    nnet3-latgen-faster$thread_string $ivector_opts \
+    nnet3-latgen-faster$thread_string $ivector_opts $frame_subsampling_opt \
      --frames-per-chunk=$frames_per_chunk \
      --minimize=$minimize --max-active=$max_active --min-active=$min_active --beam=$beam \
      --lattice-beam=$lattice_beam --acoustic-scale=$acwt --allow-partial=true \

src/chain/chain-den-graph.cc

@@ -291,7 +291,6 @@ void CreateDenominatorFst(const ContextDependency &ctx_dep,
   KALDI_LOG << "Number of states and arcs in phone-LM FST is "
             << phone_lm.NumStates() << " and " << NumArcs(phone_lm);
 
-
   int32 subsequential_symbol = trans_model.GetPhones().back() + 1;
   if (ctx_dep.CentralPosition() != ctx_dep.ContextWidth() - 1) {
     // note: this function only adds the subseq symbol to the input of what was
@@ -316,14 +315,14 @@ void CreateDenominatorFst(const ContextDependency &ctx_dep,
 
   std::vector<int32> disambig_syms_h; // disambiguation symbols on input side
   // of H -- will be empty.
-  HTransducerConfig h_cfg;
-  h_cfg.transition_scale = 0.0;  // we don't want transition probs.
-  h_cfg.push_weights = false;  // there's nothing to push.
+  HTransducerConfig h_config;
+  h_config.transition_scale = 0.0;  // we don't want transition probs.
+  h_config.push_weights = false;  // there's nothing to push.
 
   StdVectorFst *h_fst = GetHTransducer(cfst.ILabelInfo(),
                                        ctx_dep,
                                        trans_model,
-                                       h_cfg,
+                                       h_config,
                                        &disambig_syms_h);
   KALDI_ASSERT(disambig_syms_h.empty());
   StdVectorFst transition_id_fst;

src/chain/chain-kernels.cu

@@ -40,9 +40,9 @@ __device__ inline void atomic_add_thresholded(Real* address, Real value) {
   // threshold itself with probability (value / threshold).  This preserves
   // expectations.  Note: we assume that value >= 0.
 
-  // you can chose any value for the threshold, but powers of 2 are nice
+  // you can choose any value for the threshold, but powers of 2 are nice
   // because they will exactly preserve the precision of the value.
-  const Real threshold = 1.0 / (1 << 16);
+  const Real threshold = 1.0 / (1 << 14);
   if (value >= threshold) {
     atomic_add(address, value);
   } else {

src/chain/chain-supervision.cc

@@ -58,7 +58,7 @@ bool AlignmentToProtoSupervision(const SupervisionOptions &opts,
   std::vector<int32> labels(phones.size());
   int32 num_frames = std::accumulate(durations.begin(), durations.end(), 0),
       factor = opts.frame_subsampling_factor,
-      num_frames_subsampled = num_frames / factor;
+      num_frames_subsampled = (num_frames + factor - 1) / factor;
   proto_supervision->allowed_phones.clear();
   proto_supervision->allowed_phones.resize(num_frames_subsampled);
   proto_supervision->fst.DeleteStates();
@@ -69,14 +69,15 @@ bool AlignmentToProtoSupervision(const SupervisionOptions &opts,
   for (int32 i = 0; i < num_phones; i++) {
     int32 phone = phones[i], duration = durations[i];
     KALDI_ASSERT(phone > 0 && duration > 0);
-    int32 t_start_subsampled =
-        std::max<int32>(0,
-                        (current_frame - opts.left_tolerance) / factor),
-        t_end_subsampled = std::min<int32>(
-            num_frames_subsampled,
-            (current_frame + duration + opts.right_tolerance) / factor);
+    int32 t_start = std::max<int32>(0, (current_frame - opts.left_tolerance)),
+            t_end = std::min<int32>(num_frames,
+                                    (current_frame + duration + opts.right_tolerance)),
+       t_start_subsampled = (t_start + factor - 1) / factor,
+       t_end_subsampled = (t_end + factor - 1) / factor;
+
     // note: if opts.Check() passed, the following assert should pass too.
-    KALDI_ASSERT(t_end_subsampled > t_start_subsampled);
+    KALDI_ASSERT(t_end_subsampled > t_start_subsampled &&
+                 t_end_subsampled <= num_frames_subsampled);
     for (int32 t_subsampled = t_start_subsampled;
          t_subsampled < t_end_subsampled; t_subsampled++)
       proto_supervision->allowed_phones[t_subsampled].push_back(phone);
@@ -127,13 +128,7 @@ bool PhoneLatticeToProtoSupervision(const SupervisionOptions &opts,
   std::vector<int32> state_times;
   int32 num_frames = CompactLatticeStateTimes(lat, &state_times),
       factor = opts.frame_subsampling_factor,
-      num_frames_subsampled = num_frames / factor;
-  if (num_frames < opts.frame_subsampling_factor) {
-    KALDI_WARN << "Number of frames in lattice " << num_frames
-               << " is less than --frame-subsampling-factor="
-               << opts.frame_subsampling_factor;
-    return false;
-  }
+    num_frames_subsampled = (num_frames + factor - 1) / factor;
   for (int32 state = 0; state < num_states; state++)
     proto_supervision->fst.AddState();
   proto_supervision->fst.SetStart(lat.Start());
@@ -156,12 +151,11 @@ bool PhoneLatticeToProtoSupervision(const SupervisionOptions &opts,
                                     fst::StdArc(phone, phone,
                                                 fst::TropicalWeight::One(),
                                                 lat_arc.nextstate));
-      int32 t_begin_subsampled =
-          std::max<int32>(0,
-                          (state_time - opts.left_tolerance) / factor),
-          t_end_subsampled = std::min<int32>(
-              num_frames_subsampled,
-              (next_state_time + opts.right_tolerance) / factor);
+      int32 t_begin = std::max<int32>(0, (state_time - opts.left_tolerance)),
+              t_end = std::min<int32>(num_frames,
+                                      (next_state_time + opts.right_tolerance)),
+ t_begin_subsampled = (t_begin + factor - 1)/ factor,
+   t_end_subsampled = (t_end + factor - 1)/ factor;
     for (int32 t_subsampled = t_begin_subsampled;
          t_subsampled < t_end_subsampled; t_subsampled++)
       proto_supervision->allowed_phones[t_subsampled].push_back(phone);

src/chain/language-model.cc

@@ -293,6 +293,7 @@ void LanguageModelEstimator::OutputToFst(
   int64 tot_den = std::accumulate(den_counts.begin(),
                                   den_counts.end(), 0),
       tot_num = 0;  // for self-testing code.
+  double tot_logprob = 0.0;
 
   PairMapType::const_iterator
       iter = num_counts.begin(), end = num_counts.end();
@@ -304,6 +305,7 @@ void LanguageModelEstimator::OutputToFst(
     int32 den_count = den_counts[this_state];
     KALDI_ASSERT(den_count >= num_count);
     BaseFloat prob = num_count / static_cast<BaseFloat>(den_count);
+    tot_logprob += num_count * log(prob);
     if (phone > 0) {
       // it's a real phone.  find out where the transition is to.
       PairMapType::const_iterator
@@ -320,9 +322,15 @@ void LanguageModelEstimator::OutputToFst(
   }
   KALDI_ASSERT(tot_num == tot_den);
   KALDI_LOG << "Total number of phone instances seen was " << tot_num;
+  BaseFloat perplexity = exp(-(tot_logprob / tot_num));
+  KALDI_LOG << "Perplexity on training data is: " << perplexity;
+  KALDI_LOG << "Note: perplexity on unseen data will be infinity as there is "
+            << "no smoothing.  This is by design, to reduce the number of arcs.";
   fst::Connect(fst);
   // Make sure that Connect does not delete any states.
   KALDI_ASSERT(fst->NumStates() == num_states);
+  // arc-sort.  ilabel or olabel doesn't matter, it's an acceptor.
+  fst::ArcSort(fst, fst::ILabelCompare<fst::StdArc>());
   KALDI_LOG << "Created phone language model with " << num_states << " states.";
 }
 

src/chainbin/chain-est-phone-lm.cc

@@ -64,6 +64,7 @@ int main(int argc, char *argv[]) {
       const std::vector<int32> &phone_seq = phones_reader.Value();
       lm_estimator.AddCounts(phone_seq);
     }
+    KALDI_LOG << "Estimating phone LM";
     fst::StdVectorFst fst;
     lm_estimator.Estimate(&fst);