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Merge branch 'master' of https://github.com/kaldi-asr/kaldi
* 'master' of https://github.com/kaldi-asr/kaldi: [build] Bump OpenFst version to v1.6.2 (kaldi-asr#1492) [src] nnet1: fixing issue in multi-task training (kaldi-asr#1491) [egs] Fixes to URLs in vystadial example script. [egs] Minor cosmetic changes in voxforge example script (kaldi-asr#1483) [scripts,egs] sMBR on LFR xent system with shifted feats (kaldi-asr#1477) [src] bug-fix in gst plugin code (issue on g++ 5.4.0) (kaldi-asr#1479)
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| tuning/run_tdnn_lfr1c_disc.sh |
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| #!/bin/bash | ||
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| # This script does discriminative training on top of the CE nnet3 LFR system | ||
| # from run_tdnn_lfr1c. To simplify things, this assumes you are using the | ||
| # "speed-perturbed" data | ||
| # (--speed_perturb true, which is the default) in the baseline run_tdnn_d.sh script. | ||
| # | ||
| # note: this relies on having a cluster that has plenty of CPUs as well as GPUs, | ||
| # since the lattice generation runs in about real-time, so takes of the order of | ||
| # 1000 hours of CPU time. | ||
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| # Comparing effect of shift: | ||
| # System tdnn_lfr1c_sp_smbr:1 tdnn_lfr1c_sp_smbr:2 tdnn_lfr1c_sp_smbr:3 tdnn_lfr1c_sp_fs_smbr:1 tdnn_lfr1c_sp_fs_smbr:2 tdnn_lfr1c_sp_fs_smbr:3 | ||
| # WER on train_dev(tg) 16.26 16.11 16.02 16.02 15.77 15.78 | ||
| # WER on train_dev(fg) 15.01 14.91 14.80 14.79 14.58 14.50 | ||
| # WER on eval2000(tg) 18.9 18.7 18.6 18.6 18.5 18.5 | ||
| # WER on eval2000(fg) 17.4 17.2 17.1 17.1 17.0 16.9 | ||
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| set -e | ||
| set -uo pipefail | ||
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| stage=0 | ||
| train_stage=-10 # can be used to start training in the middle. | ||
| get_egs_stage=0 | ||
| use_gpu=true # for training | ||
| cleanup=false # run with --cleanup true --stage 6 to clean up (remove large things like | ||
| # alignments and degs). | ||
| degs_dir= # set this to use preexisting degs. | ||
| nj=65 # have a high number of jobs because this could take a while, and we might | ||
| # have some stragglers. | ||
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| ## Objective options | ||
| criterion=smbr | ||
| one_silence_class=true | ||
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| # you can set --disc-affix if you run different configurations, e.g. --disc-affix "_b" | ||
| # originally ran with no affix, with effective_learning_rate=0.0000125; | ||
| # reran by mistake with no affix with effective_learning_rate=0.000005 [was a bit | ||
| # better, see NOTES, but still best after 1st epoch]. | ||
| # reran again with affix=slow and effective_learning_rate=0.0000025 | ||
| # reran again with affix=slow2 and effective_learning_rate=0.00000125 (this was | ||
| # about the best). | ||
| # before checking in the script, removed the slow2 affix but left with | ||
| # the lowest learning rate. | ||
| disc_affix= | ||
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| ## Egs options. Give quite a few choices of chunk length, | ||
| ## so it can split utterances without much gap or overlap. | ||
| frames_per_eg=300,280,150,120,100 | ||
| frames_overlap_per_eg=0 | ||
| frames_per_chunk_decoding=200 | ||
| ## these context options should match the training condition. (chunk_left_context, | ||
| ## chunk_right_context) | ||
| ## We set --extra-left-context-initial 0 and --extra-right-context-final 0 | ||
| ## directly in the script below, but this should also match the training condition. | ||
| ## Note: extra-left-context and extra-right-context are 0 because this is a TDNN, | ||
| ## it's not a recurrent model like an LSTM or BLSTM. | ||
| extra_left_context=0 | ||
| extra_right_context=0 | ||
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| ## Nnet training options | ||
| effective_learning_rate=0.00000125 | ||
| max_param_change=1 | ||
| num_jobs_nnet=4 | ||
| num_epochs=3 | ||
| regularization_opts= # Applicable for providing --xent-regularize and --l2-regularize options, | ||
| # in chain models. | ||
| minibatch_size="300=32,16/150=64,32" # rule says: if chunk size is closer to 300, use minibatch size 32 (or 16 for mop-up); | ||
| # if chunk size is closer to 150, use mini atch size of 64 (or 32 for mop-up). | ||
| shift_feats=false | ||
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| ## Decode options | ||
| decode_start_epoch=1 # can be used to avoid decoding all epochs, e.g. if we decided to run more. | ||
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| . ./cmd.sh | ||
| . ./path.sh | ||
| . ./utils/parse_options.sh | ||
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| srcdir=exp/nnet3/tdnn_lfr1c_sp | ||
| graph_dir=$srcdir/graph_sw1_tg | ||
| train_data_dir=data/train_nodup_sp_hires | ||
| online_ivector_dir=exp/nnet3/ivectors_train_nodup_sp | ||
| dir=${srcdir}_${criterion}${disc_affix} | ||
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| 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 | ||
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| if [ ! -f ${srcdir}/final.mdl ]; then | ||
| echo "$0: expected ${srcdir}/final.mdl to exist" | ||
| exit 1; | ||
| fi | ||
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| frame_subsampling_factor=1 | ||
| if [ -f $srcdir/frame_subsampling_factor ]; then | ||
| frame_subsampling_factor=$(cat $srcdir/frame_subsampling_factor) | ||
| fi | ||
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| affix= # Will be set if doing input frame shift | ||
| if [[ "$shift_feats" = true && $frame_subsampling_factor -ne 1 ]]; then | ||
| if [ $stage -le 0 ]; then | ||
| utils/data/shift_and_combine_feats.sh --write-utt2orig $dir/utt2orig \ | ||
| $frame_subsampling_factor $train_data_dir ${train_data_dir}_fs | ||
| steps/online/nnet2/copy_ivector_dir.sh --utt2orig $dir/utt2orig \ | ||
| $online_ivector_dir ${online_ivector_dir}_fs | ||
| rm $dir/utt2orig | ||
| fi | ||
| online_ivector_dir=${online_ivector_dir}_fs | ||
| train_data_dir=${train_data_dir}_fs | ||
| affix=_fs | ||
| fi | ||
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| if [ $stage -le 1 ]; then | ||
| # hardcode no-GPU for alignment, although you could use GPU [you wouldn't | ||
| # get excellent GPU utilization though.] | ||
| steps/nnet3/align.sh --cmd "$decode_cmd" --use-gpu false \ | ||
| --scale-opts '--transition-scale=1.0 --acoustic-scale=0.333 --self-loop-scale=0.333' \ | ||
| --frames-per-chunk $frames_per_chunk_decoding \ | ||
| --extra-left-context $extra_left_context --extra-right-context $extra_right_context \ | ||
| --extra-left-context-initial 0 --extra-right-context-final 0 \ | ||
| --online-ivector-dir $online_ivector_dir \ | ||
| --nj $nj $train_data_dir data/lang $srcdir ${srcdir}_ali${affix} ; | ||
| fi | ||
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| if [ -z "$degs_dir" ]; then | ||
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| if [ $stage -le 2 ]; then | ||
| if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d ${srcdir}_degs/storage ]; then | ||
| utils/create_split_dir.pl \ | ||
| /export/b{09,10,11,12}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5/${srcdir}_degs/storage ${srcdir}_degs/storage | ||
| fi | ||
| if [ -d ${srcdir}_degs/storage ]; then max_copy_jobs=10; else max_copy_jobs=5; fi | ||
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| steps/nnet3/get_degs.sh \ | ||
| --cmd "$decode_cmd --mem 10G" --num-threads 3 \ | ||
| --self-loop-scale 0.333 --acwt 0.333 \ | ||
| --max-copy-jobs $max_copy_jobs \ | ||
| --extra-left-context $extra_left_context \ | ||
| --extra-right-context $extra_right_context \ | ||
| --extra-left-context-initial 0 --extra-right-context-final 0 \ | ||
| --frames-per-chunk-decoding "$frames_per_chunk_decoding" \ | ||
| --stage $get_egs_stage \ | ||
| --online-ivector-dir $online_ivector_dir \ | ||
| --frames-per-eg $frames_per_eg --frames-overlap-per-eg $frames_overlap_per_eg \ | ||
| $train_data_dir data/lang ${srcdir} ${srcdir}_ali${affix} ${srcdir}_degs${affix} || exit 1 | ||
| fi | ||
| fi | ||
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| if [ $stage -le 3 ]; then | ||
| [ -z "$degs_dir" ] && degs_dir=${srcdir}_degs${affix} | ||
| steps/nnet3/train_discriminative.sh --cmd "$decode_cmd" \ | ||
| --stage $train_stage \ | ||
| --acoustic-scale 0.333 \ | ||
| --effective-lrate $effective_learning_rate --max-param-change $max_param_change \ | ||
| --criterion $criterion --drop-frames true \ | ||
| --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" \ | ||
| ${degs_dir} $dir | ||
| fi | ||
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| if [ $stage -le 4 ]; then | ||
| for x in `seq $decode_start_epoch $num_epochs`; do | ||
| for decode_set in train_dev eval2000; do | ||
| num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l` | ||
| for iter in epoch$x epoch${x}_adj; do | ||
| ( | ||
| steps/nnet3/decode.sh --nj $num_jobs --cmd "$decode_cmd" --iter $iter \ | ||
| --acwt 0.333 --post-decode-acwt 3.0 \ | ||
| --online-ivector-dir exp/nnet3/ivectors_${decode_set} \ | ||
| $graph_dir data/${decode_set}_hires $dir/decode_${decode_set}_sw1_tg_${iter} || exit 1; | ||
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| steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \ | ||
| data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \ | ||
| $dir/decode_${decode_set}_sw1_{tg,fsh_fg}_${iter} || exit 1; | ||
| ) & | ||
| done | ||
| done | ||
| done | ||
| fi | ||
| wait; | ||
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| if [ $stage -le 5 ] && $cleanup; then | ||
| # if you run with "--cleanup true --stage 6" you can clean up. | ||
| # actually, keep the alignments in case we need them later.. they're slow to | ||
| # create, and quite big. | ||
| # rm ${srcdir}_ali/ali.*.gz || true | ||
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| steps/nnet2/remove_egs.sh ${srcdir}_degs || true | ||
| fi | ||
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| wait; | ||
| exit 0; |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,48 @@ | ||
| #!/bin/bash | ||
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| # Copyright 2017 Johns Hopkins University (author: Hossein Hadian) | ||
| # Apache 2.0 | ||
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| # This script copies the necessary parts of an online ivector directory | ||
| # optionally applying a mapping to the ivector_online.scp file | ||
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| utt2orig= | ||
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| . utils/parse_options.sh | ||
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| if [ $# != 2 ]; then | ||
| echo "Usage: " | ||
| echo " $0 [options] <srcdir> <destdir>" | ||
| echo "e.g.:" | ||
| echo " $0 exp/nnet3/online_ivector_train exp/nnet3/online_ivector_train_fs" | ||
| echo "Options" | ||
| echo " --utt2orig=<file> # utterance id mapping to use" | ||
| exit 1; | ||
| fi | ||
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| srcdir=$1 | ||
| destdir=$2 | ||
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| if [ ! -f $srcdir/ivector_period ]; then | ||
| echo "$0: no such file $srcdir/ivector_period" | ||
| exit 1; | ||
| fi | ||
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| if [ "$destdir" == "$srcdir" ]; then | ||
| echo "$0: this script requires <srcdir> and <destdir> to be different." | ||
| exit 1 | ||
| fi | ||
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| set -e; | ||
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| mkdir -p $destdir | ||
| cp -r $srcdir/{conf,ivector_period} $destdir | ||
| if [ -z $utt2orig ]; then | ||
| cp $srcdir/ivector_online.scp $destdir | ||
| else | ||
| utils/apply_map.pl -f 2 $srcdir/ivector_online.scp < $utt2orig > $destdir/ivector_online.scp | ||
| fi | ||
| cp $srcdir/final.ie.id $destdir | ||
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| echo "$0: Copied necessary parts of online ivector directory $srcdir to $destdir" |
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