adding changes for language modelling

egs/iam/v2/local/chain/run_cnn_e2eali.sh

@@ -1 +1 @@
-tuning/run_cnn_e2eali_1c.sh
+tuning/run_cnn_e2eali_1d.sh

egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1c.sh

@@ -26,7 +26,7 @@ stage=0
 nj=30
 train_set=train
 nnet3_affix= # affix for exp dirs, e.g. it was _cleaned in tedlium.
-affix=_1b6 #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
+affix=_1c #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
 e2echain_model_dir=exp/chain/e2e_cnn_1a
 common_egs_dir=
 reporting_email=

egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1d.sh

@@ -0,0 +1,246 @@
+#!/bin/bash
+
+# e2eali_1c is the same as e2eali_1b but has fewer CNN layers, smaller
+# l2-regularize, more epochs and uses dropout.
+
+
+# local/chain/compare_wer.sh exp/chain/cnn_e2eali_1b exp/chain/cnn_e2eali_1c
+# System cnn_e2eali_1b cnn_e2eali_1c
+# WER 10.33 10.05
+# WER (rescored) 10.10 9.75
+# CER 5.00 4.76
+# CER (rescored) 4.88 4.68
+# Final train prob -0.0428 -0.0317
+# Final valid prob -0.0666 -0.0630
+# Final train prob (xent) -0.9210 -0.5413
+# Final valid prob (xent) -1.0264 -0.7096
+# Parameters 3.98M 5.12M
+
+# steps/info/chain_dir_info.pl exp/chain/cnn_e2eali_1c
+# exp/chain/cnn_e2eali_1c: num-iters=21 nj=2..4 num-params=5.1M dim=40->392 combine=-0.034->-0.034 (over 1) xent:train/valid[13,20,final]=(-0.953,-0.800,-0.541/-1.03,-0.933,-0.710) logprob:train/valid[13,20,final]=(-0.069,-0.048,-0.032/-0.091,-0.078,-0.063)
+
+set -e -o pipefail
+
+stage=0
+
+nj=30
+train_set=train
+nnet3_affix= # affix for exp dirs, e.g. it was _cleaned in tedlium.
+affix=_1d #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
+e2echain_model_dir=exp/chain/e2e_cnn_1a
+common_egs_dir=
+reporting_email=
+
+# chain options
+train_stage=-10
+xent_regularize=0.1
+frame_subsampling_factor=4
+# training chunk-options
+chunk_width=340,300,200,100
+num_leaves=500
+# we don't need extra left/right context for TDNN systems.
+chunk_left_context=0
+chunk_right_context=0
+tdnn_dim=550
+# training options
+srand=0
+remove_egs=true
+#lang_decode=data.new/lang
+#lang_rescore=data.new/lang_rescore_6g
+lang_decode=data/lang
+lang_rescore=data/lang_rescore_6g
+
+dropout_schedule='0,[email protected],[email protected],0'
+# End configuration section.
+echo "$0 $@" # Print the command line for logging
+
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+
+if ! cuda-compiled; then
+ cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+ali_dir=exp/chain/e2e_ali_train
+lat_dir=exp/chain${nnet3_affix}/e2e_${train_set}_lats
+dir=exp/chain${nnet3_affix}/cnn_e2eali${affix}
+#dir=exp/chain/cnn_e2eali_1c
+train_data_dir=data/${train_set}
+tree_dir=exp/chain${nnet3_affix}/tree_e2e
+
+# the 'lang' directory is created by this script.
+# If you create such a directory with a non-standard topology
+# you should probably name it differently.
+lang=data/lang_chain
+for f in $train_data_dir/feats.scp $ali_dir/ali.1.gz $ali_dir/final.mdl; do
+ [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+
+if [ $stage -le 1 ]; then
+ echo "$0: creating lang directory $lang with chain-type topology"
+ # Create a version of the lang/ directory that has one state per phone in the
+ # topo file. [note, it really has two states.. the first one is only repeated
+ # once, the second one has zero or more repeats.]
+ if [ -d $lang ]; then
+ if [ $lang/L.fst -nt data/lang/L.fst ]; then
+ echo "$0: $lang already exists, not overwriting it; continuing"
+ else
+ echo "$0: $lang already exists and seems to be older than data/lang..."
+ echo " ... not sure what to do. Exiting."
+ exit 1;
+ fi
+ else
+ cp -r data/lang $lang
+ silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+ nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+ # Use our special topology... note that later on may have to tune this
+ # topology.
+ steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+ fi
+fi
+
+if [ $stage -le 2 ]; then
+ # Get the alignments as lattices (gives the chain training more freedom).
+ # use the same num-jobs as the alignments
+ steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
+ --acoustic-scale 1.0 \
+ --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
+ ${train_data_dir} data/lang $e2echain_model_dir $lat_dir
+ echo "" >$lat_dir/splice_opts
+fi
+
+if [ $stage -le 3 ]; then
+ # Build a tree using our new topology. We know we have alignments for the
+ # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+ # those. The num-leaves is always somewhat less than the num-leaves from
+ # the GMM baseline.
+ if [ -f $tree_dir/final.mdl ]; then
+ echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+ exit 1;
+ fi
+
+ steps/nnet3/chain/build_tree.sh \
+ --frame-subsampling-factor $frame_subsampling_factor \
+ --alignment-subsampling-factor 1 \
+ --context-opts "--context-width=2 --central-position=1" \
+ --cmd "$cmd" $num_leaves ${train_data_dir} \
+ $lang $ali_dir $tree_dir
+fi
+
+
+if [ $stage -le 4 ]; then
+ mkdir -p $dir
+ echo "$0: creating neural net configs using the xconfig parser";
+
+ num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
+ learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+ cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
+ tdnn_opts="l2-regularize=0.03"
+ output_opts="l2-regularize=0.04"
+ common1="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
+ common2="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
+ common3="$cnn_opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=90"
+ mkdir -p $dir/configs
+ cat <<EOF > $dir/configs/network.xconfig
+ input dim=40 name=input
+
+ conv-relu-batchnorm-dropout-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
+ conv-relu-batchnorm-dropout-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
+ conv-relu-batchnorm-dropout-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+ conv-relu-batchnorm-dropout-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+ conv-relu-batchnorm-dropout-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common3 height-subsample-out=2
+ relu-batchnorm-dropout-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim $tdnn_opts dropout-proportion=0.0
+ relu-batchnorm-dropout-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts dropout-proportion=0.0
+ relu-batchnorm-dropout-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts dropout-proportion=0.0
+
+ ## adding the layers for chain branch
+ relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $tdnn_opts
+ output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $output_opts
+ relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim target-rms=0.5 $tdnn_opts
+ output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5 $output_opts
+EOF
+ steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+
+if [ $stage -le 5 ]; then
+ if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+ utils/create_split_dir.pl \
+ /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+ fi
+
+ steps/nnet3/chain/train.py --stage=$train_stage \
+ --cmd="$cmd" \
+ --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+ --chain.xent-regularize $xent_regularize \
+ --chain.leaky-hmm-coefficient=0.1 \
+ --chain.l2-regularize=0.00005 \
+ --chain.apply-deriv-weights=true \
+ --chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
+ --chain.frame-subsampling-factor=$frame_subsampling_factor \
+ --chain.alignment-subsampling-factor=1 \
+ --chain.left-tolerance 3 \
+ --chain.right-tolerance 3 \
+ --trainer.srand=$srand \
+ --trainer.max-param-change=2.0 \
+ --trainer.num-epochs=8 \
+ --trainer.frames-per-iter=1500000 \
+ --trainer.optimization.num-jobs-initial=2 \
+ --trainer.optimization.num-jobs-final=4 \
+ --trainer.dropout-schedule $dropout_schedule \
+ --trainer.optimization.initial-effective-lrate=0.001 \
+ --trainer.optimization.final-effective-lrate=0.0001 \
+ --trainer.optimization.shrink-value=1.0 \
+ --trainer.num-chunk-per-minibatch=32,16 \
+ --trainer.optimization.momentum=0.0 \
+ --egs.chunk-width=$chunk_width \
+ --egs.chunk-left-context=$chunk_left_context \
+ --egs.chunk-right-context=$chunk_right_context \
+ --egs.chunk-left-context-initial=0 \
+ --egs.chunk-right-context-final=0 \
+ --egs.dir="$common_egs_dir" \
+ --egs.opts="--frames-overlap-per-eg 0 --constrained false" \
+ --cleanup.remove-egs=$remove_egs \
+ --use-gpu=true \
+ --reporting.email="$reporting_email" \
+ --feat-dir=$train_data_dir \
+ --tree-dir=$tree_dir \
+ --lat-dir=$lat_dir \
+ --dir=$dir || exit 1;
+fi
+
+if [ $stage -le 6 ]; then
+ # The reason we are using data/lang here, instead of $lang, is just to
+ # emphasize that it's not actually important to give mkgraph.sh the
+ # lang directory with the matched topology (since it gets the
+ # topology file from the model). So you could give it a different
+ # lang directory, one that contained a wordlist and LM of your choice,
+ # as long as phones.txt was compatible.
+
+ utils/mkgraph.sh \
+ --self-loop-scale 1.0 $lang_decode \
+ $dir $dir/graph || exit 1;
+fi
+
+if [ $stage -le 7 ]; then
+ frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+ steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+ --extra-left-context $chunk_left_context \
+ --extra-right-context $chunk_right_context \
+ --extra-left-context-initial 0 \
+ --extra-right-context-final 0 \
+ --frames-per-chunk $frames_per_chunk \
+ --nj $nj --cmd "$cmd" \
+ $dir/graph data/test $dir/decode_test || exit 1;
+
+ steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
+ data/test $dir/decode_test{,_rescored} || exit 1
+fi

egs/iam/v2/local/process_corpus.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+# Copyright 2018 Ashish Arora
+# Apache 2.0
+# This script reads valid phones and removes the lines in the corpus
+# which have any other phone.
+
+import os
+import sys, io
+
+phone_file = os.path.join('data/local/phones.txt')
+infile = io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8')
+output = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+phone_dict = dict()
+with open(phone_file, 'r', encoding='utf-8') as phone_fh:
+ for line in phone_fh:
+ line = line.strip().split()[0]
+ phone_dict[line] = line
+
+phone_dict[' '] = ' '
+corpus_text = list()
+for line in infile:
+ text = line.strip()
+ skip_text = False
+ for phone in text:
+ if phone not in phone_dict.keys():
+ skip_text = True
+ break
+ if not skip_text:
+ output.write(text+ '\n')
+