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trunk,nnet1 : adding the tandem recipe,
git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@4742 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
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Karel Vesely
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@ -4,8 +4,9 @@
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# Switchboard portion of eval2000, excluding CallHome, which is
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# substantially easier.
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for x in exp/{mono,tri,sgmm,nnet}*/decode*; do [ -d $x ] && grep Sum $x/score_*/*.sys | utils/best_wer.sh; done 2>/dev/null
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for x in exp/{mono,tri,sgmm,nnet}*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done 2>/dev/null
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for x in exp/{mono,tri,sgmm,nnet,dnn}*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done 2>/dev/null
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for x in exp/{mono,tri,sgmm,nnet,dnn}*/decode*; do [ -d $x ] && grep Sum $x/score_*/*.sys | utils/best_wer.sh; done 2>/dev/null
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exit 0
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%WER 36.7 | 1831 21395 | 66.3 24.2 9.5 3.0 36.7 68.4 | exp/tri1/decode_eval2000_sw1_fsh_tgpr/score_12/eval2000.ctm.swbd.filt.sys
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@ -0,0 +1,3 @@
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beam=16.0
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lattice_beam=8.0
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scoring_opts="--min-lmwt 20 --max-lmwt 30"
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@ -0,0 +1 @@
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--sample-frequency=8000
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@ -0,0 +1,232 @@
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#!/bin/bash
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# Copyright 2012-2014 Brno University of Technology (Author: Karel Vesely)
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# Apache 2.0
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# This example script trains a bottleneck feature extractor with
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# 'Universal Context' topology as invented by Frantisek Grezl,
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# the network is on top of FBANK+f0 features.
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. cmd.sh
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. path.sh
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# Config:
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stage=0 # resume training with --stage=N
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# End of config.
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. utils/parse_options.sh || exit 1;
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#
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if [ $stage -le 1 ]; then
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# prepare the FBANK+f0 features
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# eval2000
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dir=data-fbank-pitch/eval2000; srcdir=data/eval2000
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(mkdir -p $dir; cp $srcdir/* $dir; )
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steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj 20 $dir $dir/log $dir/data || exit 1;
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steps/compute_cmvn_stats.sh $dir $dir/log $dir/data || exit 1;
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# training set
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dir=data-fbank-pitch/train; srcdir=data/train
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(mkdir -p $dir; cp $srcdir/* $dir; )
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steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj 20 $dir $dir/log $dir/data || exit 1;
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steps/compute_cmvn_stats.sh $dir $dir/log $dir/data || exit 1;
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fi
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if [ $stage -le 2 ]; then
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# Prepare same subsets as in the main MFCC-GMM recipe, these will be used
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# during during building GMM system from flat-start, later in the Tandem recipe.
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data=data-fbank-pitch
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# Use the first 4k sentences as dev set. Note: when we trained the LM, we used
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utils/subset_data_dir.sh --first $data/train 4000 $data/train_dev # 5hr 6min
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n=$[`cat data/train/segments | wc -l` - 4000]
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utils/subset_data_dir.sh --last $data/train $n $data/train_nodev
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# Prepare data for training mono
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utils/subset_data_dir.sh --shortest $data/train_nodev 100000 $data/train_100kshort
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utils/subset_data_dir.sh $data/train_100kshort 10000 $data/train_10k
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local/remove_dup_utts.sh 100 $data/train_10k $data/train_10k_nodup
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# Take the first 30k utterances (about 1/8th of the data)
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utils/subset_data_dir.sh --first $data/train_nodev 30000 $data/train_30k
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local/remove_dup_utts.sh 200 $data/train_30k $data/train_30k_nodup
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# Take the first 100k utterances (just under half the data); we'll use
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# this for later stages of training.
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utils/subset_data_dir.sh --first $data/train_nodev 100000 $data/train_100k
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local/remove_dup_utts.sh 200 $data/train_100k $data/train_100k_nodup
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# Full training dataset,
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local/remove_dup_utts.sh 300 $data/train_nodev $data/train_nodup
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# split the data : 90% train 10% cross-validation (held-out)
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dir=$data/train_nodup
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utils/subset_data_dir_tr_cv.sh $dir ${dir}_tr90 ${dir}_cv10 || exit 1
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fi
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#########################################################################################
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# Let's build universal-context bottleneck network
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# - Universal context MLP is a hierarchy of two bottleneck neural networks
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# - The first network can see a limited range of frames (11 frames)
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# - The second network sees concatenation of bottlneck outputs of the first
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# network, with temporal shifts -10 -5 0 5 10, (in total a range of 31 frames
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# in the original feature space)
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# - This structure has been reported to produce superior performance
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# compared to a network with single bottleneck
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#
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if [ $stage -le 3 ]; then
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# 1st network, overall context +/-5 frames
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# - the topology is 90_1500_1500_80_1500_NSTATES, linear bottleneck
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dir=exp/nnet5b_uc-part1
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ali=exp/tri4b_ali_nodup
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$cuda_cmd $dir/log/train_nnet.log \
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steps/nnet/train.sh --hid-layers 2 --hid-dim 1500 --bn-dim 80 --apply-cmvn true \
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--copy-feats false \
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--feat-type traps --splice 5 --traps-dct-basis 6 --learn-rate 0.008 \
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data-fbank-pitch/train_nodup_tr90 data-fbank-pitch/train_nodup_cv10 data/lang ${ali} ${ali} $dir || exit 1;
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fi
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if [ $stage -le 4 ]; then
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# Compose feature_transform for the next stage,
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# - remaining part of the first network is fixed
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dir=exp/nnet5b_uc-part1
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feature_transform=$dir/final.feature_transform.part1
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nnet-concat $dir/final.feature_transform \
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"nnet-copy --remove-last-layers=4 --binary=false $dir/final.nnet - |" \
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"utils/nnet/gen_splice.py --fea-dim=80 --splice=2 --splice-step=5 |" \
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$feature_transform || exit 1
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# 2nd network, overall context +/-15 frames
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# - the topology will be 400_1500_1500_30_1500_NSTATES, again, the bottleneck is linear
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dir=exp/nnet5b_uc-part2
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ali=exp/tri4b_ali_nodup
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$cuda_cmd $dir/log/train_nnet.log \
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steps/nnet/train.sh --hid-layers 2 --hid-dim 1500 --bn-dim 30 --apply-cmvn true \
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--feature-transform $feature_transform --learn-rate 0.008 \
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data-fbank-pitch/train_nodup_tr90 data-fbank-pitch/train_nodup_cv10 data/lang ${ali} ${ali} $dir || exit 1;
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fi
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#
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#########################################################################################
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if [ $stage -le 5 ]; then
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# Store the BN-features
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data=data-bn/nnet5b_uc-part2
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srcdata=data-fbank-pitch/
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nnet=exp/nnet5b_uc-part2
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# eval2000
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steps/nnet/make_bn_feats.sh --cmd "$train_cmd" --nj 20 $data/eval2000 $srcdata/eval2000 \
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$nnet $data/eval2000/log $data/eval2000/data || exit 1
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# trainig data (full set)
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steps/nnet/make_bn_feats.sh --cmd "$train_cmd" --nj 40 $data/train $srcdata/train \
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$nnet $data/train/log $data/train/data || exit 1
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# Compute CMVN of the BN-features
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dir=data-bn/nnet5b_uc-part2/train
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steps/compute_cmvn_stats.sh $dir $dir/log $dir/data || exit 1;
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dir=data-bn/nnet5b_uc-part2/eval2000
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steps/compute_cmvn_stats.sh $dir $dir/log $dir/data || exit 1;
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fi
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if [ $stage -le 6 ]; then
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# Prepare BN-feature subsets same as with MFCCs in run.sh
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data=data-bn/nnet5b_uc-part2/
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# Use the first 4k sentences as dev set.
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utils/subset_data_dir.sh --first $data/train 4000 $data/train_dev # 5hr 6min
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n=$[`cat data/train/segments | wc -l` - 4000]
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utils/subset_data_dir.sh --last $data/train $n $data/train_nodev
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# Prepare data for training mono
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utils/subset_data_dir.sh --shortest $data/train_nodev 100000 $data/train_100kshort
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utils/subset_data_dir.sh $data/train_100kshort 10000 $data/train_10k
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local/remove_dup_utts.sh 100 $data/train_10k $data/train_10k_nodup
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# Take the first 30k utterances (about 1/8th of the data)
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utils/subset_data_dir.sh --first $data/train_nodev 30000 $data/train_30k
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local/remove_dup_utts.sh 200 $data/train_30k $data/train_30k_nodup
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# Take the first 100k utterances (just under half the data); we'll use
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# this for later stages of training.
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utils/subset_data_dir.sh --first $data/train_nodev 100000 $data/train_100k
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local/remove_dup_utts.sh 200 $data/train_100k $data/train_100k_nodup
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# Full dataset
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local/remove_dup_utts.sh 300 $data/train_nodev $data/train_nodup
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fi
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# Start building the tandem GMM system
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# - train from mono to tri4b, run bmmi training
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bndata=data-bn/nnet5b_uc-part2/
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if [ $stage -le 7 ]; then
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steps/tandem/train_mono.sh --nj 10 --cmd "$train_cmd" \
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data/train_10k_nodup $bndata/train_10k_nodup data/lang exp/tandem2uc-mono0a || exit 1;
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steps/tandem/align_si.sh --nj 30 --cmd "$train_cmd" \
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data/train_30k_nodup $bndata/train_30k_nodup data/lang exp/tandem2uc-mono0a exp/tandem2uc-mono0a_ali || exit 1;
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steps/tandem/train_deltas.sh --cmd "$train_cmd" \
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3200 30000 data/train_30k_nodup $bndata/train_30k_nodup data/lang exp/tandem2uc-mono0a_ali exp/tandem2uc-tri1 || exit 1;
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utils/mkgraph.sh data/lang_test exp/tandem2uc-tri1 exp/tandem2uc-tri1/graph
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steps/tandem/decode.sh --nj 30 --cmd "$decode_cmd" --config conf/decode_tandem.config \
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exp/tandem2uc-tri1/graph data/eval2000 $bndata/eval2000 exp/tandem2uc-tri1/decode_eval2000
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fi
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if [ $stage -le 8 ]; then
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steps/tandem/align_si.sh --nj 30 --cmd "$train_cmd" \
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data/train_30k_nodup $bndata/train_30k_nodup data/lang exp/tandem2uc-tri1 exp/tandem2uc-tri1_ali || exit 1;
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steps/tandem/train_deltas.sh --cmd "$train_cmd" \
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3200 30000 data/train_30k_nodup $bndata/train_30k_nodup data/lang exp/tandem2uc-tri1_ali exp/tandem2uc-tri2 || exit 1;
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utils/mkgraph.sh data/lang_test exp/tandem2uc-tri2 exp/tandem2uc-tri2/graph || exit 1;
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steps/tandem/decode.sh --nj 30 --cmd "$decode_cmd" --config conf/decode_tandem.config \
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exp/tandem2uc-tri2/graph data/eval2000 $bndata/eval2000 exp/tandem2uc-tri2/decode_eval2000 || exit 1;
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fi
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if [ $stage -le 9 ]; then
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steps/tandem/align_si.sh --nj 30 --cmd "$train_cmd" \
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data/train_100k_nodup $bndata/train_100k_nodup data/lang exp/tandem2uc-tri2 exp/tandem2uc-tri2_ali || exit 1;
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# Train tri3b, which is LDA+MLLT, on 100k_nodup data.
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steps/tandem/train_lda_mllt.sh --cmd "$train_cmd" \
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--splice-opts "--left-context=3 --right-context=3" \
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5500 90000 data/train_100k_nodup $bndata/train_100k_nodup data/lang exp/tandem2uc-tri2_ali exp/tandem2uc-tri3b || exit 1;
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utils/mkgraph.sh data/lang_test exp/tandem2uc-tri3b exp/tandem2uc-tri3b/graph || exit 1;
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steps/tandem/decode.sh --nj 30 --cmd "$decode_cmd" --config conf/decode_tandem.config \
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exp/tandem2uc-tri3b/graph data/eval2000 $bndata/eval2000 exp/tandem2uc-tri3b/decode_eval2000 || exit 1;
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fi
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if [ $stage -le 10 ]; then
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# From now, we start building a more serious system (with SAT),
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# and we'll do the alignment with fMLLR.
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steps/tandem/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
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data/train_nodup $bndata/train_nodup data/lang exp/tandem2uc-tri3b exp/tandem2uc-tri3b_ali_nodup || exit 1;
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steps/tandem/train_sat.sh --cmd "$train_cmd" \
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11500 200000 data/train_nodup $bndata/train_nodup data/lang exp/tandem2uc-tri3b_ali_nodup exp/tandem2uc-tri4b || exit 1;
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utils/mkgraph.sh data/lang_test exp/tandem2uc-tri4b exp/tandem2uc-tri4b/graph || exit 1
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steps/tandem/decode_fmllr.sh --nj 30 --cmd "$decode_cmd" --config conf/decode_tandem.config \
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exp/tandem2uc-tri4b/graph data/eval2000 $bndata/eval2000 exp/tandem2uc-tri4b/decode_eval2000 || exit 1
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fi
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# bMMI starting from system in tandem2uc-tri4b, use full dataset.
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if [ $stage -le 11 ]; then
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steps/tandem/align_fmllr.sh --nj 100 --cmd "$train_cmd" \
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data/train_nodup $bndata/train_nodup data/lang exp/tandem2uc-tri4b exp/tandem2uc-tri4b_ali || exit 1;
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steps/tandem/make_denlats.sh --nj 40 --cmd "$decode_cmd" --transform-dir exp/tandem2uc-tri4b_ali \
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--sub-split 100 data/train_nodup $bndata/train_nodup data/lang exp/tandem2uc-tri4b exp/tandem2uc-tri4b_denlats || exit 1;
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fi
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if [ $stage -le 12 ]; then
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steps/tandem/train_mmi.sh --cmd "$decode_cmd" --boost 0.1 --acwt 0.039 \
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data/train_nodup $bndata/train_nodup data/lang exp/tandem2uc-tri4b_{ali,denlats} exp/tandem2uc-tri4b_mmi_b0.1 || exit 1;
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steps/tandem/decode.sh --nj 30 --cmd "$decode_cmd" --config conf/decode_tandem.config \
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--transform-dir exp/tandem2uc-tri4b/decode_eval2000 \
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exp/tandem2uc-tri4b/graph data/eval2000 $bndata/eval2000 exp/tandem2uc-tri4b_mmi_b0.1/decode_eval2000 || exit 1;
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fi
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echo success
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exit 0
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@ -374,7 +374,7 @@ done
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# # Karel's DNN recipe on top of fMLLR features
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# local/run_dnn.sh
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# local/nnet/run_dnn.sh
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# # Dan's nnet recipe
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