French ASR system

cmd.sh

+# you can change cmd.sh depending on what type of queue you are using.
+# If you have no queueing system and want to run on a local machine, you
+# can change all instances 'queue.pl' to run.pl (but be careful and run
+# commands one by one: most recipes will exhaust the memory on your
+# machine).  queue.pl works with GridEngine (qsub).  slurm.pl works
+# with slurm.  Different queues are configured differently, with different
+# queue names and different ways of specifying things like memory;
+# to account for these differences you can create and edit the file
+# conf/queue.conf to match your queue's configuration.  Search for
+# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information,
+# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl.
+
+#export train_cmd="queue.pl --mem 2G"
+#export decode_cmd="queue.pl --mem 4G"
+#export mkgraph_cmd="queue.pl --mem 8G"
+
+# Local machine
+export train_cmd="run.pl --mem 2G"
+export decode_cmd="run.pl --mem 4G"
+export mkgraph_cmd="run.pl --mem 8G"

conf/decode.config

+# empty config, just use the defaults.

conf/mfcc.conf

+--use-energy=false   # only non-default option.

conf/mfcc_hires.conf

+# config for high-resolution MFCC features, intended for neural network training
+# Note: we keep all cepstra, so it has the same info as filterbank features,
+# but MFCC is more easily compressible (because less correlated) which is why 
+# we prefer this method.
+--use-energy=false   # use average of log energy, not energy.
+--num-mel-bins=40     # similar to Google's setup.
+--num-ceps=40     # there is no dimensionality reduction.
+--low-freq=20     # low cutoff frequency for mel bins... this is high-bandwidth data, so
+                  # there might be some information at the low end.
+--high-freq=-400 # high cutoff frequently, relative to Nyquist of 8000 (=7600) 

conf/online_cmvn.conf

+# configuration file for apply-cmvn-online, used in the script ../local/run_online_decoding.sh

conf/online_pitch.conf

+## This config is given by conf/make_pitch_online.sh to the program compute-and-process-kaldi-pitch-feats,
+## and is copied by steps/online/nnet2/prepare_online_decoding.sh and similar scripts, to be given
+## to programs like online2-wav-nnet2-latgen-faster.
+## The program compute-and-process-kaldi-pitch-feats will use it to compute pitch features that
+## are the same as that those which will generated in online decoding; this enables us to train
+## in a way that's compatible with online decoding.
+## 
+
+## most of these options relate to the post-processing rather than the pitch
+## extraction itself.
+--add-raw-log-pitch=true   ## this is intended for input to neural nets, so our
+                           ## approach is "throw everything in and see what
+                           ## sticks".
+--normalization-left-context=75
+--normalization-right-context=50 # We're removing some of the right-context
+                                 # for the normalization.   Would normally be 75.
+                                 #
+                                 # Note: our changes to the (left,right) context
+                                 # from the defaults of (75,75) to (75,50) will
+                                 # almost certainly worsen results, but will
+                                 # reduce latency.
+--frames-per-chunk=10    ## relates to offline simulation of online decoding; 1
+                         ## would be equivalent to getting in samples one by
+                         ## one.
+--simulate-first-pass-online=true  ## this make the online-pitch-extraction code
+                                   ## output the 'first-pass' features, which
+                                   ## are less accurate than the final ones, and
+                                   ## which are the only features the neural-net
+                                   ## decoding would ever see (since we can't
+                                   ## afford to do lattice rescoring in the
+                                   ## neural-net code

conf/queue_no_k20.conf

+# Default configuration
+command qsub -v PATH -cwd -S /bin/bash -j y -l arch=*64*
+option mem=* -l mem_free=$0,ram_free=$0
+option mem=0          # Do not add anything to qsub_opts
+option num_threads=* -pe smp $0
+option num_threads=1  # Do not add anything to qsub_opts
+option max_jobs_run=* -tc $0
+default gpu=0
+option gpu=0 -q all.q
+option gpu=* -l gpu=$0 -q g.q
+default allow_k20=true
+option allow_k20=true
+option allow_k20=false -l 'hostname=!g01*&!g02*&!b06*'

local/lm/normalize.txt

+
+
+#num2Words
+pip install num2words
+python setup.py install
+python setup.py test
+from num2words import num2words
+num2words(42, lang='fr')

path.sh

+export KALDI_ROOT=`pwd`/../../..
+export PATH=$PWD/tools/festival/nsw/bin:$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
+[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
+. $KALDI_ROOT/tools/config/common_path.sh
+export LC_ALL=C
+
+# we use this both in the (optional) LM training and the G2P-related scripts
+PYTHON='python2.7'
+
+### Below are the paths used by the optional parts of the recipe
+
+# We only need the Festival stuff below for the optional text normalization(for LM-training) step
+FEST_ROOT=tools/festival
+NSW_PATH=${FEST_ROOT}/festival/bin:${FEST_ROOT}/nsw/bin
+export PATH=$PATH:$NSW_PATH
+
+# SRILM is needed for LM model building
+SRILM_ROOT=$KALDI_ROOT/tools/srilm
+SRILM_PATH=$SRILM_ROOT/bin:$SRILM_ROOT/bin/i686-m64
+export PATH=$PATH:$SRILM_PATH
+
+# Sequitur G2P executable
+sequitur=$KALDI_ROOT/tools/sequitur/g2p.py
+sequitur_path="$(dirname $sequitur)/lib/$PYTHON/site-packages"
+
+# Directory under which the LM training corpus should be extracted
+LM_CORPUS_ROOT=./lm-corpus

run.sh

+#!/bin/bash
+
+
+# data dir
+data=/data/tcof/
+
+
+. ./cmd.sh
+. ./path.sh
+
+# you might not want to do this for interactive shells.
+set -e
+
+# format the data as Kaldi data directories
+for part in train dev test ; do
+  # use underscore-separated names in data directories.
+  local/data_prep.sh $data/LibriSpeech/$part data/$part
+done
+
+## Optional text corpus normalization and LM training
+## These scripts are here primarily as a documentation of the process that has been
+## used to build the LM. Most users of this recipe will NOT need/want to run
+## this step. The pre-built language models and the pronunciation lexicon, as
+## well as some intermediate data(e.g. the normalized text used for LM training),
+## are available for download at http://www.openslr.org/11/
+#local/lm/train_lm.sh $LM_CORPUS_ROOT \
+#  data/local/lm/norm/tmp data/local/lm/norm/norm_texts data/local/lm
+
+## Optional G2P training scripts.
+## As the LM training scripts above, this script is intended primarily to
+## document our G2P model creation process
+#local/g2p/train_g2p.sh data/local/dict/cmudict data/local/lm
+
+# when "--stage 3" option is used below we skip the G2P steps, and use the
+# lexicon we have already downloaded from openslr.org/11/
+local/prepare_dict.sh --stage 3 --nj 30 --cmd "$train_cmd" \
+   data/local/lm data/local/lm data/local/dict_nosp
+
+utils/prepare_lang.sh data/local/dict_nosp \
+  "<UNK>" data/local/lang_tmp_nosp data/lang_nosp
+
+local/format_lms.sh --src-dir data/lang_nosp data/local/lm
+
+# Create ConstArpaLm format language model for full 3-gram and 4-gram LMs
+utils/build_const_arpa_lm.sh data/local/lm/lm_tglarge.arpa.gz \
+  data/lang_nosp data/lang_nosp_test_tglarge
+utils/build_const_arpa_lm.sh data/local/lm/lm_fglarge.arpa.gz \
+  data/lang_nosp data/lang_nosp_test_fglarge
+
+mfccdir=mfcc
+# spread the mfccs over various machines, as this data-set is quite large.
+if [[  $(hostname -f) ==  *.clsp.jhu.edu ]]; then
+  mfcc=$(basename mfccdir) # in case was absolute pathname (unlikely), get basename.
+  utils/create_split_dir.pl /export/b{02,11,12,13}/$USER/kaldi-data/egs/librispeech/s5/$mfcc/storage \
+    $mfccdir/storage
+fi
+
+
+for part in dev_clean test_clean dev_other test_other train_clean_100; do
+  steps/make_mfcc.sh --cmd "$train_cmd" --nj 40 data/$part exp/make_mfcc/$part $mfccdir
+  steps/compute_cmvn_stats.sh data/$part exp/make_mfcc/$part $mfccdir
+done
+
+# Make some small data subsets for early system-build stages.  Note, there are 29k
+# utterances in the train_clean_100 directory which has 100 hours of data.
+# For the monophone stages we select the shortest utterances, which should make it
+# easier to align the data from a flat start.
+
+utils/subset_data_dir.sh --shortest data/train_clean_100 2000 data/train_2kshort
+utils/subset_data_dir.sh data/train_clean_100 5000 data/train_5k
+utils/subset_data_dir.sh data/train_clean_100 10000 data/train_10k
+
+# train a monophone system
+steps/train_mono.sh --boost-silence 1.25 --nj 20 --cmd "$train_cmd" \
+  data/train_2kshort data/lang_nosp exp/mono
+
+# decode using the monophone model
+(
+  utils/mkgraph.sh --mono data/lang_nosp_test_tgsmall \
+    exp/mono exp/mono/graph_nosp_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode.sh --nj 20 --cmd "$decode_cmd" exp/mono/graph_nosp_tgsmall \
+      data/$test exp/mono/decode_nosp_tgsmall_$test
+  done
+)&
+
+steps/align_si.sh --boost-silence 1.25 --nj 10 --cmd "$train_cmd" \
+  data/train_5k data/lang_nosp exp/mono exp/mono_ali_5k
+
+# train a first delta + delta-delta triphone system on a subset of 5000 utterances
+steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \
+    2000 10000 data/train_5k data/lang_nosp exp/mono_ali_5k exp/tri1
+
+# decode using the tri1 model
+(
+  utils/mkgraph.sh data/lang_nosp_test_tgsmall \
+    exp/tri1 exp/tri1/graph_nosp_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode.sh --nj 20 --cmd "$decode_cmd" exp/tri1/graph_nosp_tgsmall \
+      data/$test exp/tri1/decode_nosp_tgsmall_$test
+    steps/lmrescore.sh --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tgmed} \
+      data/$test exp/tri1/decode_nosp_{tgsmall,tgmed}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tglarge} \
+      data/$test exp/tri1/decode_nosp_{tgsmall,tglarge}_$test
+  done
+)&
+
+steps/align_si.sh --nj 10 --cmd "$train_cmd" \
+  data/train_10k data/lang_nosp exp/tri1 exp/tri1_ali_10k
+
+
+# train an LDA+MLLT system.
+steps/train_lda_mllt.sh --cmd "$train_cmd" \
+   --splice-opts "--left-context=3 --right-context=3" 2500 15000 \
+   data/train_10k data/lang_nosp exp/tri1_ali_10k exp/tri2b
+
+# decode using the LDA+MLLT model
+(
+  utils/mkgraph.sh data/lang_nosp_test_tgsmall \
+    exp/tri2b exp/tri2b/graph_nosp_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode.sh --nj 20 --cmd "$decode_cmd" exp/tri2b/graph_nosp_tgsmall \
+      data/$test exp/tri2b/decode_nosp_tgsmall_$test
+    steps/lmrescore.sh --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tgmed} \
+      data/$test exp/tri2b/decode_nosp_{tgsmall,tgmed}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tglarge} \
+      data/$test exp/tri2b/decode_nosp_{tgsmall,tglarge}_$test
+  done
+)&
+
+# Align a 10k utts subset using the tri2b model
+steps/align_si.sh  --nj 10 --cmd "$train_cmd" --use-graphs true \
+  data/train_10k data/lang_nosp exp/tri2b exp/tri2b_ali_10k
+
+# Train tri3b, which is LDA+MLLT+SAT on 10k utts
+steps/train_sat.sh --cmd "$train_cmd" 2500 15000 \
+  data/train_10k data/lang_nosp exp/tri2b_ali_10k exp/tri3b
+
+# decode using the tri3b model
+(
+  utils/mkgraph.sh data/lang_nosp_test_tgsmall \
+    exp/tri3b exp/tri3b/graph_nosp_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode_fmllr.sh --nj 20 --cmd "$decode_cmd" \
+      exp/tri3b/graph_nosp_tgsmall data/$test \
+      exp/tri3b/decode_nosp_tgsmall_$test
+    steps/lmrescore.sh --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tgmed} \
+      data/$test exp/tri3b/decode_nosp_{tgsmall,tgmed}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tglarge} \
+      data/$test exp/tri3b/decode_nosp_{tgsmall,tglarge}_$test
+  done
+)&
+
+# align the entire train_clean_100 subset using the tri3b model
+steps/align_fmllr.sh --nj 20 --cmd "$train_cmd" \
+  data/train_clean_100 data/lang_nosp \
+  exp/tri3b exp/tri3b_ali_clean_100
+
+# train another LDA+MLLT+SAT system on the entire 100 hour subset
+steps/train_sat.sh  --cmd "$train_cmd" 4200 40000 \
+  data/train_clean_100 data/lang_nosp \
+  exp/tri3b_ali_clean_100 exp/tri4b
+
+# decode using the tri4b model
+(
+  utils/mkgraph.sh data/lang_nosp_test_tgsmall \
+    exp/tri4b exp/tri4b/graph_nosp_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode_fmllr.sh --nj 20 --cmd "$decode_cmd" \
+      exp/tri4b/graph_nosp_tgsmall data/$test \
+      exp/tri4b/decode_nosp_tgsmall_$test
+    steps/lmrescore.sh --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tgmed} \
+      data/$test exp/tri4b/decode_nosp_{tgsmall,tgmed}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,tglarge} \
+      data/$test exp/tri4b/decode_nosp_{tgsmall,tglarge}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_nosp_test_{tgsmall,fglarge} \
+      data/$test exp/tri4b/decode_nosp_{tgsmall,fglarge}_$test
+  done
+)&
+
+# Now we compute the pronunciation and silence probabilities from training data,
+# and re-create the lang directory.
+steps/get_prons.sh --cmd "$train_cmd" \
+  data/train_clean_100 data/lang_nosp exp/tri4b
+utils/dict_dir_add_pronprobs.sh --max-normalize true \
+  data/local/dict_nosp \
+  exp/tri4b/pron_counts_nowb.txt exp/tri4b/sil_counts_nowb.txt \
+  exp/tri4b/pron_bigram_counts_nowb.txt data/local/dict
+
+utils/prepare_lang.sh data/local/dict \
+  "<UNK>" data/local/lang_tmp data/lang
+local/format_lms.sh --src-dir data/lang data/local/lm
+
+utils/build_const_arpa_lm.sh \
+  data/local/lm/lm_tglarge.arpa.gz data/lang data/lang_test_tglarge
+utils/build_const_arpa_lm.sh \
+  data/local/lm/lm_fglarge.arpa.gz data/lang data/lang_test_fglarge
+
+# decode using the tri4b model with pronunciation and silence probabilities
+(
+  utils/mkgraph.sh \
+    data/lang_test_tgsmall exp/tri4b exp/tri4b/graph_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode_fmllr.sh --nj 20 --cmd "$decode_cmd" \
+      exp/tri4b/graph_tgsmall data/$test \
+      exp/tri4b/decode_tgsmall_$test
+    steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
+      data/$test exp/tri4b/decode_{tgsmall,tgmed}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
+      data/$test exp/tri4b/decode_{tgsmall,tglarge}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
+      data/$test exp/tri4b/decode_{tgsmall,fglarge}_$test
+  done
+)&
+
+# align train_clean_100 using the tri4b model
+steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
+  data/train_clean_100 data/lang exp/tri4b exp/tri4b_ali_clean_100
+
+# if you want at this point you can train and test NN model(s) on the 100 hour
+# subset
+local/nnet2/run_5a_clean_100.sh
+
+local/download_and_untar.sh $data $data_url train-clean-360
+
+# now add the "clean-360" subset to the mix ...
+local/data_prep.sh \
+  $data/LibriSpeech/train-clean-360 data/train_clean_360
+steps/make_mfcc.sh --cmd "$train_cmd" --nj 40 data/train_clean_360 \
+  exp/make_mfcc/train_clean_360 $mfccdir
+steps/compute_cmvn_stats.sh \
+  data/train_clean_360 exp/make_mfcc/train_clean_360 $mfccdir
+
+# ... and then combine the two sets into a 460 hour one
+utils/combine_data.sh \
+  data/train_clean_460 data/train_clean_100 data/train_clean_360
+
+# align the new, combined set, using the tri4b model
+steps/align_fmllr.sh --nj 40 --cmd "$train_cmd" \
+  data/train_clean_460 data/lang exp/tri4b exp/tri4b_ali_clean_460
+
+# create a larger SAT model, trained on the 460 hours of data.
+steps/train_sat.sh  --cmd "$train_cmd" 5000 100000 \
+  data/train_clean_460 data/lang exp/tri4b_ali_clean_460 exp/tri5b
+
+# decode using the tri5b model
+(
+  utils/mkgraph.sh data/lang_test_tgsmall \
+    exp/tri5b exp/tri5b/graph_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode_fmllr.sh --nj 20 --cmd "$decode_cmd" \
+      exp/tri5b/graph_tgsmall data/$test \
+      exp/tri5b/decode_tgsmall_$test
+    steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
+      data/$test exp/tri5b/decode_{tgsmall,tgmed}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
+      data/$test exp/tri5b/decode_{tgsmall,tglarge}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
+      data/$test exp/tri5b/decode_{tgsmall,fglarge}_$test
+  done
+)&
+
+# train a NN model on the 460 hour set
+local/nnet2/run_6a_clean_460.sh
+
+local/download_and_untar.sh $data $data_url train-other-500
+
+# prepare the 500 hour subset.
+local/data_prep.sh \
+  $data/LibriSpeech/train-other-500 data/train_other_500
+steps/make_mfcc.sh --cmd "$train_cmd" --nj 40 data/train_other_500 \
+  exp/make_mfcc/train_other_500 $mfccdir
+steps/compute_cmvn_stats.sh \
+  data/train_other_500 exp/make_mfcc/train_other_500 $mfccdir
+
+# combine all the data
+utils/combine_data.sh \
+  data/train_960 data/train_clean_460 data/train_other_500
+
+steps/align_fmllr.sh --nj 40 --cmd "$train_cmd" \
+  data/train_960 data/lang exp/tri5b exp/tri5b_ali_960
+
+# train a SAT model on the 960 hour mixed data.  Use the train_quick.sh script
+# as it is faster.
+steps/train_quick.sh --cmd "$train_cmd" \
+  7000 150000 data/train_960 data/lang exp/tri5b_ali_960 exp/tri6b
+
+# decode using the tri6b model
+(
+  utils/mkgraph.sh data/lang_test_tgsmall \
+    exp/tri6b exp/tri6b/graph_tgsmall
+  for test in test_clean test_other dev_clean dev_other; do
+    steps/decode_fmllr.sh --nj 20 --cmd "$decode_cmd" \
+      exp/tri6b/graph_tgsmall data/$test exp/tri6b/decode_tgsmall_$test
+    steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
+      data/$test exp/tri6b/decode_{tgsmall,tgmed}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
+      data/$test exp/tri6b/decode_{tgsmall,tglarge}_$test
+    steps/lmrescore_const_arpa.sh \
+      --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
+      data/$test exp/tri6b/decode_{tgsmall,fglarge}_$test
+  done
+)&
+
+# this does some data-cleaning. The cleaned data should be useful when we add
+# the neural net and chain systems.
+local/run_cleanup_segmentation.sh
+
+# steps/cleanup/debug_lexicon.sh --remove-stress true  --nj 200 --cmd "$train_cmd" data/train_clean_100 \
+#    data/lang exp/tri6b data/local/dict/lexicon.txt exp/debug_lexicon_100h
+
+# #Perform rescoring of tri6b be means of faster-rnnlm
+# #Attention: with default settings requires 4 GB of memory per rescoring job, so commenting this out by default
+# wait && local/run_rnnlm.sh \
+#     --rnnlm-ver "faster-rnnlm" \
+#     --rnnlm-options "-hidden 150 -direct 1000 -direct-order 5" \
+#     --rnnlm-tag "h150-me5-1000" $data data/local/lm
+
+# #Perform rescoring of tri6b be means of faster-rnnlm using Noise contrastive estimation
+# #Note, that could be extremely slow without CUDA
+# #We use smaller direct layer size so that it could be stored in GPU memory (~2Gb)
+# #Suprisingly, bottleneck here is validation rather then learning
+# #Therefore you can use smaller validation dataset to speed up training
+# wait && local/run_rnnlm.sh \
+#     --rnnlm-ver "faster-rnnlm" \
+#     --rnnlm-options "-hidden 150 -direct 400 -direct-order 3 --nce 20" \
+#     --rnnlm-tag "h150-me3-400-nce20" $data data/local/lm
+
+
+# train nnet3 tdnn models on the entire data with data-cleaning (xent and chain)
+local/chain/run_tdnn.sh # set "--stage 11" if you have already run local/nnet3/run_tdnn.sh
+
+# The nnet3 TDNN recipe:
+# local/nnet3/run_tdnn.sh # set "--stage 11" if you have already run local/chain/run_tdnn.sh
+
+# # train models on cleaned-up data
+# # we've found that this isn't helpful-- see the comments in local/run_data_cleaning.sh
+# local/run_data_cleaning.sh
+
+# # The following is the current online-nnet2 recipe, with "multi-splice".
+# local/online/run_nnet2_ms.sh
+
+# # The following is the discriminative-training continuation of the above.
+# local/online/run_nnet2_ms_disc.sh
+
+# ## The following is an older version of the online-nnet2 recipe, without "multi-splice".  It's faster
+# ## to train but slightly worse.
+# # local/online/run_nnet2.sh
+
+# Wait for decodings in the background
+wait

steps

+../../wsj/s5/steps
\ No newline at end of file

utils

+../../wsj/s5/utils
\ No newline at end of file