зеркало из https://github.com/mozilla/kaldi.git
trunk: removed minor bugs from swbd/s5b and added multisplice results to RESULTS
git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@4843 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
This commit is contained in:
Vijayaditya Peddinti
Родитель
bfb0489168
Коммит
41a8f9b3cb
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@ -130,7 +130,13 @@ $WER 15.6 | 1831 21395 | 86.2 9.2 4.5 1.9 15.6 53.0 | exp/nnet5f_gpu/decode_eva
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%WER 14.9 | 1831 21395 | 87.4 9.1 3.5 2.4 14.9 52.3 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_tg_epoch4/score_15/eval2000.ctm.swbd.filt.sys
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# Multi-splice recipe
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%WER 13.8 | 1831 21395 | 87.4 8.0 4.6 1.2 13.8 51.9 | exp/nnet2_online/nnet_ms_a/decode_eval2000_hires_sw1_fsh_tgpr/score_12/eval2000_hires.ctm.swbd.filt.sys
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%WER 14.5 | 1831 21395 | 86.8 8.4 4.8 1.3 14.5 51.9 | exp/nnet2_online/nnet_ms_a/decode_eval2000_hires_sw1_tg/score_12/eval2000_hires.ctm.swbd.filt.sys
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%WER 13.9 | 1831 21395 | 87.3 8.1 4.6 1.2 13.9 51.8 | exp/nnet2_online/nnet_ms_a_online/decode_eval2000_hires_sw1_fsh_tgpr/score_12/eval2000_hires.ctm.swbd.filt.sys
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%WER 14.2 | 1831 21395 | 87.1 8.3 4.6 1.3 14.2 51.5 | exp/nnet2_online/nnet_ms_a_online/decode_eval2000_hires_sw1_fsh_tgpr_per_utt/score_12/eval2000_hires.ctm.swbd.filt.sys
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%WER 14.5 | 1831 21395 | 86.9 8.6 4.6 1.4 14.5 52.2 | exp/nnet2_online/nnet_ms_a_online/decode_eval2000_hires_sw1_tg/score_11/eval2000_hires.ctm.swbd.filt.sys
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%WER 14.7 | 1831 21395 | 86.7 8.7 4.6 1.4 14.7 51.7 | exp/nnet2_online/nnet_ms_a_online/decode_eval2000_hires_sw1_tg_per_utt/score_12/eval2000_hires.ctm.swbd.filt.sys
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# Below are the results from the Karel's DNN recipe (Oct 11 2013) from 'local/run_dnn.sh'
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# DNN with RBM pre-training, 6hidden layers, 2048 neurons each, ~9k outputs
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@ -141,3 +147,5 @@ $WER 15.6 | 1831 21395 | 86.2 9.2 4.5 1.9 15.6 53.0 | exp/nnet5f_gpu/decode_eva
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%WER 14.1 | 1831 21395 | 87.8 8.7 3.5 1.9 14.1 50.4 | exp/tri4b_pretrain-dbn_dnn_smbr_iter1-lats/decode_eval2000_sw1_fsh_tgpr_it2/score_14/eval2000.ctm.swbd.filt.sys
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# Final system rescored by sw1_fsh trigram (unpruned)
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%WER 13.4 | 1831 21395 | 88.4 8.2 3.4 1.8 13.4 49.2 | exp/tri4b_pretrain-dbn_dnn_smbr_iter1-lats/decode_eval2000_sw1_fsh_tg.3_it2/score_14/eval2000.ctm.swbd.filt.sys
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@ -88,7 +88,7 @@ if [ $stage -le 5 ]; then
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steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 data/train_hires_nodup data/train_hires_nodup_max2
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steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 30 \
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data/train_hires_nodup_max2 exp/nnet2_online/extractor exp/nnet2_online/ivectors_train_nodup2 || exit 1;
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data/train_hires_nodup_max2 exp/nnet2_online/extractor exp/nnet2_online/ivectors_train_hires_nodup2 || exit 1;
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fi
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@ -43,7 +43,7 @@ if [ $stage -le 6 ]; then
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--num-epochs 5 --num-jobs-initial 3 --num-jobs-final 18 \
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--num-hidden-layers 6 --splice-indexes "$splice_indexes" \
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--feat-type raw \
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--online-ivector-dir exp/nnet2_online/ivectors_train_nodup2 \
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--online-ivector-dir exp/nnet2_online/ivectors_train_hires_nodup2 \
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--cmvn-opts "--norm-means=false --norm-vars=false" \
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--num-threads "$num_threads" \
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--minibatch-size "$minibatch_size" \
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@ -94,7 +94,7 @@ if [ $stage -le 10 ]; then
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for lm_suffix in tg fsh_tgpr; do
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graph_dir=exp/tri4b/graph_sw1_${lm_suffix}
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for data in eval2000_hires train_hires_dev; do
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steps/online/nnet2/decode.sh --config conf/decode.config
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steps/online/nnet2/decode.sh --config conf/decode.config \
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--cmd "$decode_cmd" --nj 30 \
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"$graph_dir" data/${data} \
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${dir}_online/decode_${data}_sw1_${lm_suffix} || exit 1;
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@ -137,10 +137,9 @@ if [ $stage -le 5 ]; then
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graph_dir=exp/tri4b/graph_sw1_${lm_suffix}
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for epoch in $(seq $decode_start_epoch $num_epochs); do
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for test in eval2000_hires train_hires_dev; do
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(
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steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 50 \
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steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
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--per-utt true \
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--iter epoch$epoch "$graph_dir" data/${test} $dir/decode_epoch${epoch}_${test}_sw1_${lm_suffix}_per_utt || exit 1
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) &
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done
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done
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done
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@ -1,213 +0,0 @@
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#!/bin/bash
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. ./cmd.sh
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set -e
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stage=1
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train_stage=-10
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use_gpu=true
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nnet2_online=nnet2_online_ms
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splice_indexes="layer0/-4:-3:-2:-1:0:1:2:3:4 layer2/-5:-3:3"
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common_egs_dir=
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. ./path.sh
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. ./utils/parse_options.sh
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if $use_gpu; then
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if ! cuda-compiled; then
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cat <<EOF && exit 1
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This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
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If you want to use GPUs (and have them), go to src/, and configure and make on a machine
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where "nvcc" is installed. Otherwise, call this script with --use-gpu false
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EOF
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fi
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parallel_opts="-l gpu=1"
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num_threads=1
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minibatch_size=512
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# the _a is in case I want to change the parameters.
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else
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# Use 4 nnet jobs just like run_4d_gpu.sh so the results should be
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# almost the same, but this may be a little bit slow.
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num_threads=16
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minibatch_size=128
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parallel_opts="-pe smp $num_threads"
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fi
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dir=exp/$nnet2_online/nnet_a
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mkdir -p exp/$nnet2_online
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if [ $stage -le 1 ]; then
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mfccdir=mfcc_hires
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if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
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date=$(date +'%m_%d_%H_%M')
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utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/swbd-$date/s5b/$mfccdir/storage $mfccdir/storage
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fi
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utils/copy_data_dir.sh data/train data/train_hires
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steps/make_mfcc.sh --nj 70 --mfcc-config conf/mfcc_hires.conf \
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--cmd "$train_cmd" data/train_hires exp/make_hires/train $mfccdir;
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steps/compute_cmvn_stats.sh data/train_hires exp/make_hires/train $mfccdir;
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# Remove the small number of utterances that couldn't be extracted for some
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# reason (e.g. too short; no such file).
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utils/fix_data_dir.sh data/train_hires;
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# Create MFCCs for the eval set
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utils/copy_data_dir.sh data/eval2000 data/eval2000_hires
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steps/make_mfcc.sh --cmd "$train_cmd" --nj 10 --mfcc-config conf/mfcc_hires.conf \
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data/eval2000_hires exp/make_hires/eval2000 $mfccdir;
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steps/compute_cmvn_stats.sh data/eval2000_hires exp/make_hires/eval2000 $mfccdir;
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utils/fix_data_dir.sh data/eval2000_hires # remove segments with problems
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# Use the first 4k sentences as dev set. Note: when we trained the LM, we used
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# the 1st 10k sentences as dev set, so the 1st 4k won't have been used in the
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# LM training data. However, they will be in the lexicon, plus speakers
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# may overlap, so it's still not quite equivalent to a test set.
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utils/subset_data_dir.sh --first data/train_hires 4000 data/train_hires_dev ;# 5hr 6min
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n=$[`cat data/train/segments | wc -l` - 4000]
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utils/subset_data_dir.sh --last data/train_hires $n data/train_hires_nodev ;
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# Take the first 30k utterances (about 1/8th of the data) this will be used
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# for the diagubm training
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utils/subset_data_dir.sh --first data/train_hires_nodev 30000 data/train_hires_30k
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local/remove_dup_utts.sh 200 data/train_hires_30k data/train_hires_30k_nodup # 33hr
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# create a 100k subset for the lda+mllt training
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utils/subset_data_dir.sh --first data/train_hires_nodev 100000 data/train_hires_100k;
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local/remove_dup_utts.sh 200 data/train_hires_100k data/train_hires_100k_nodup;
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local/remove_dup_utts.sh 300 data/train_hires_nodev data/train_hires_nodup # 286hr
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fi
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if [ $stage -le 2 ]; then
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# We need to build a small system just because we need the LDA+MLLT transform
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# to train the diag-UBM on top of. We use --num-iters 13 because after we get
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# the transform (12th iter is the last), any further training is pointless.
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# this decision is based on fisher_english
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steps/train_lda_mllt.sh --cmd "$train_cmd" --num-iters 13 \
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--splice-opts "--left-context=3 --right-context=3" \
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5500 90000 data/train_hires_100k_nodup data/lang exp/tri2_ali_100k_nodup exp/$nnet2_online/tri3b
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fi
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if [ $stage -le 3 ]; then
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# To train a diagonal UBM we don't need very much data, so use the smallest subset.
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steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 --num-frames 200000 \
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data/train_hires_30k_nodup 512 exp/$nnet2_online/tri3b exp/$nnet2_online/diag_ubm
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fi
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if [ $stage -le 4 ]; then
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# iVector extractors can be sensitive to the amount of data, but this one has a
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# fairly small dim (defaults to 100) so we don't use all of it, we use just the
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# 100k subset (just under half the data).
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steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
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data/train_hires_100k_nodup exp/$nnet2_online/diag_ubm exp/$nnet2_online/extractor || exit 1;
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fi
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if [ $stage -le 5 ]; then
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# We extract iVectors on all the train_nodup data, which will be what we
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# train the system on.
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# having a larger number of speakers is helpful for generalization, and to
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# handle per-utterance decoding well (iVector starts at zero).
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steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 data/train_hires_nodup data/train_hires_nodup_max2
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steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 30 \
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data/train_hires_nodup_max2 exp/$nnet2_online/extractor exp/$nnet2_online/ivectors_train_nodup2 || exit 1;
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fi
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if [ $stage -le 6 ]; then
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# Because we have a lot of data here and we don't want the training to take
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# too long so we reduce the number of epochs from the defaults (15 + 5) to (5
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# + 2), and the (initial,final) learning rate from the defaults (0.04, 0.004)
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# to (0.01, 0.001).
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# decided to let others run their jobs too (we only have 10 GPUs on our queue
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# at JHU). The number of parameters is smaller than the baseline system we had in
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# mind (../nnet2/run_5d_gpu.sh, which had pnorm input/output dim 3000/300 and
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# 5 hidden layers, versus our 3000/300 and 5 hidden layers, even though we're
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# training on more data than the baseline system. The motivation here is that we
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# want to demonstrate the capability of doing real-time decoding, and if the
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# network was too bug we wouldn't be able to decode in real-time using a CPU.
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steps/nnet2/train_pnorm_multisplice.sh --stage $train_stage \
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--splice-indexes "$splice_indexes" \
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--feat-type raw \
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--online-ivector-dir exp/$nnet2_online/ivectors_train_nodup2 \
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--cmvn-opts "--norm-means=false --norm-vars=false" \
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--num-threads "$num_threads" \
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--minibatch-size "$minibatch_size" \
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--parallel-opts "$parallel_opts" \
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--num-jobs-nnet 6 \
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--num-epochs 5 \
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--add-layers-period 1 \
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--num-hidden-layers 4 \
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--mix-up 4000 \
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--initial-learning-rate 0.01 --final-learning-rate 0.001 \
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--cmd "$decode_cmd" \
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--egs-dir "$common_egs_dir" \
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--pnorm-input-dim 3000 \
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--pnorm-output-dim 300 \
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data/train_hires_nodup data/lang exp/tri4b_ali_nodup $dir || exit 1;
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fi
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if [ $stage -le 7 ]; then
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for data in eval2000_hires train_hires_dev; do
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steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 20 \
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data/${data} exp/$nnet2_online/extractor exp/$nnet2_online/ivectors_${data} || exit 1;
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done
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fi
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if [ $stage -le 8 ]; then
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# this does offline decoding that should give the same results as the real
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# online decoding (the one with --per-utt true)
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for lm_suffix in tg fsh_tgpr; do
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graph_dir=exp/tri4b/graph_sw1_${lm_suffix}
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# use already-built graphs.
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for data in eval2000_hires train_hires_dev; do
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steps/nnet2/decode.sh --nj 30 --cmd "$decode_cmd" --config conf/decode.config \
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--online-ivector-dir exp/$nnet2_online/ivectors_${data} \
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$graph_dir data/${data} $dir/decode_${data}_sw1_${lm_suffix} || exit 1;
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done
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done
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fi
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if [ $stage -le 9 ]; then
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# If this setup used PLP features, we'd have to give the option --feature-type plp
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# to the script below.
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steps/online/nnet2/prepare_online_decoding.sh --mfcc-config conf/mfcc_hires.conf \
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data/lang exp/$nnet2_online/extractor "$dir" ${dir}_online || exit 1;
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fi
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if [ $stage -le 10 ]; then
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# do the actual online decoding with iVectors, carrying info forward from
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# previous utterances of the same speaker.
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for lm_suffix in tg fsh_tgpr; do
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graph_dir=exp/tri4b/graph_sw1_${lm_suffix}
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for data in eval2000_hires train_hires_dev; do
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steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
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"$graph_dir" data/${data} ${dir}_online/decode_${data}_sw1_${lm_suffix} || exit 1;
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done
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done
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fi
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if [ $stage -le 11 ]; then
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# this version of the decoding treats each utterance separately
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# without carrying forward speaker information.
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for lm_suffix in tg fsh_tgpr; do
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graph_dir=exp/tri4b/graph_sw1_${lm_suffix}
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for data in eval2000_hires train_hires_dev; do
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steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
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--per-utt true \
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"$graph_dir" data/${data} ${dir}_online/decode_${data}_sw1_${lm_suffix}_per_utt || exit 1;
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done
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done
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fi
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exit 0;
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# get results on Dev with this command:
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for x in exp/$nnet2_online/nnet_a/decode_train_dev_sw1_*; do grep WER $x/wer_* | utils/best_wer.sh; done
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_mfcc_hires# and results on eval2000 with this command:
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for x in exp/$nnet2_online/nnet_a/decode_eval2000_*; do grep Sum $x/score_*/*sys | utils/best_wer.sh; done
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