зеркало из https://github.com/mozilla/kaldi.git
sandbox/online: adding some new files from trunk that were skipped in the last merge.
git-svn-id: https://svn.code.sf.net/p/kaldi/code/sandbox/online@4319 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
This commit is contained in:
Dan Povey
Родитель
7d8da8bb56
Коммит
2568ec23c1
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#!/bin/bash
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. cmd.sh
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# This step interpolates a small RNNLM (with weight 0.15) with the 4-gram LM.
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
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0.15 data/lang_test_bd_fg data/local/rnnlm-hs.h30.voc10k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs30_0.15 \
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|| exit 1;
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
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0.3 data/lang_test_bd_fg data/local/rnnlm-hs.h100.voc20k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs100_0.3 \
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|| exit 1;
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
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0.3 data/lang_test_bd_fg data/local/rnnlm-hs.h300.voc30k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs300_0.3 \
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|| exit 1;
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
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0.3 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3 \
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|| exit 1;
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
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0.3 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3_N1000
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
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0.3 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3_N1000 \
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|| exit 1;
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dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.4_N1000
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rm -rf $dir
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cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3_N1000 $dir
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--stage 7 --N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
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0.4 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg $dir
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dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.4
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rm -rf $dir
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cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3 $dir
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--stage 7 --N 100 --cmd "$decode_cmd" --inv-acwt 17 \
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0.4 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg $dir
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dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.15
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rm -rf $dir
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cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3 $dir
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--stage 7 --N 100 --cmd "$decode_cmd" --inv-acwt 17 \
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0.15 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg $dir
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--N 10 --cmd "$decode_cmd" --inv-acwt 17 \
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0.3 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3_N10 \
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|| exit 1;
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dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.4_N1000
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rm -rf $dir
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cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3_N1000 $dir
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--stage 7 --N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
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0.4 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg $dir
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dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.15_N1000
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rm -rf $dir
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cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3_N1000 $dir
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--stage 7 --N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
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0.15 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg $dir
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dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.5_N1000
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rm -rf $dir
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cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm-hs400_0.3_N1000 $dir
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steps/rnnlmrescore.sh --rnnlm_ver rnnlm-hs-0.1b \
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--stage 7 --N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
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0.5 data/lang_test_bd_fg data/local/rnnlm-hs.h400.voc40k data/test_eval92 \
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exp/tri3b/decode_bd_tgpr_eval92_fg $dir
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#!/bin/bash
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# Copyright 2014 Johns Hopkins University (Author: Daniel Povey).
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# Apache 2.0.
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# This script removes the examples in an egs/ directory, e.g.
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# steps/nnet2/remove_egs.sh exp/nnet4b/egs/
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# We give it its own script because we need to be careful about
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# things that are soft links to something in storage/ (i.e. remove the
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# data that's linked to as well as the soft link), and we want to not
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# delete the examples if someone has done "touch $dir/egs/.nodelete".
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if [ $# != 1 ]; then
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echo "Usage: $0 <egs-dir>"
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echo "e.g.: $0 data/nnet4b/egs/"
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echo "This script is usually equivalent to 'rm <egs-dir>/egs.*' but it handles following"
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echo "soft links to <egs-dir>/storage/, and avoids deleting anything in the directory if"
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echo "someone did 'touch <egs-dir>/.nodelete"
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exit 1;
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fi
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egs=$1
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if [ ! -d $egs ]; then
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echo "$0: expected directory $egs to exist"
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exit 1;
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fi
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if [ -f $egs/.nodelete ]; then
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echo "$0: not delting egs in $egs since $egs/.nodelete exists"
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exit 0;
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fi
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flist=$egs/egs.*.ark
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if [ "$flist" == '$egs/egs.*.ark' ]; then # did not expand..
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echo "$0: directory $egs does not seem to contain egs"
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exit 1;
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fi
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for f in $flist; do
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if [ -L $f ]; then
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rm $(readlink $f) # this will print a warning if it fails.
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fi
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rm $f
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done
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echo "$0: Finished deleting examples in $egs"
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#!/bin/bash
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# Copyright 2014 Johns Hopkins University (Author: Daniel Povey).
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# Apache 2.0.
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# This script further trains an already-existing neural network,
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# given an existing model and an examples (egs/) directory.
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# The number of parallel jobs (--num-jobs-nnet) is determined by the
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# egs directory.
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# Begin configuration section.
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cmd=run.pl
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num_epochs=10 # Number of epochs of training; number of iterations is
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# worked out from this.
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num_iters_final=20 # Maximum number of final iterations to give to the
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# optimization over the validation set.
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learning_rate_factor=1.0 # You can use this to gradually decrease the learning
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# rate during training (e.g. use 0.2); the initial
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# learning rates are as specified in the model, but it
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# will decrease slightly on each iteration to achieve
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# this ratio.
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combine=true # controls whether or not to do the final model combination.
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combine_regularizer=1.0e-14 # Small regularizer so that parameters won't go crazy.
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minibatch_size=128 # by default use a smallish minibatch size for neural net
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# training; this controls instability which would otherwise
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# be a problem with multi-threaded update. Note: it also
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# interacts with the "preconditioned" update which generally
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# works better with larger minibatch size, so it's not
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# completely cost free.
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shuffle_buffer_size=5000 # This "buffer_size" variable controls randomization of the samples
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# on each iter. You could set it to 0 or to a large value for complete
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# randomization, but this would both consume memory and cause spikes in
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# disk I/O. Smaller is easier on disk and memory but less random. It's
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# not a huge deal though, as samples are anyway randomized right at the start.
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mix_up=0
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stage=-5
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num_threads=16
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parallel_opts="-pe smp 16 -l ram_free=1G,mem_free=1G" # by default we use 16 threads; this lets the queue know.
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# note: parallel_opts doesn't automatically get adjusted if you adjust num-threads.
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cleanup=true
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remove_egs=false
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prior_subset_size=10000 # 10k samples per job, for computing priors. Should be
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# more than enough.
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# End configuration section.
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echo "$0 $@" # Print the command line for logging
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if [ -f path.sh ]; then . ./path.sh; fi
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. parse_options.sh || exit 1;
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if [ $# != 3 ]; then
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echo "Usage: $0 [opts] <input-model> <egs-dir> <exp-dir>"
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echo " e.g.: $0 exp/nnet4c/final.mdl exp/nnet4c/egs exp/nnet5c/"
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echo "see also the older script update_nnet.sh which creates the egs itself"
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echo ""
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echo "Main options (for others, see top of script file)"
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echo " --config <config-file> # config file containing options"
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echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
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echo " --num-epochs <#epochs|15> # Number of epochs of training"
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echo " # while reducing learning rate (determines #iterations, together"
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echo " # with --samples-per-iter and --num-jobs-nnet)"
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echo " --num-epochs-extra <#epochs-extra|5> # Number of extra epochs of training"
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echo " # after learning rate fully reduced"
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echo " --learning-rate-factor<factor|1.0> # Factor (e.g. 0.2) by which to change learning rate"
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echo " # during the course of training"
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echo " --num-threads <num-threads|16> # Number of parallel threads per job (will affect results"
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echo " # as well as speed; may interact with batch size; if you increase"
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echo " # this, you may want to decrease the batch size."
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echo " --parallel-opts <opts|\"-pe smp 16 -l ram_free=1G,mem_free=1G\"> # extra options to pass to e.g. queue.pl for processes that"
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echo " # use multiple threads... note, you might have to reduce mem_free,ram_free"
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echo " # versus your defaults, because it gets multiplied by the -pe smp argument."
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echo " --minibatch-size <minibatch-size|128> # Size of minibatch to process (note: product with --num-threads"
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echo " # should not get too large, e.g. >2k)."
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echo " --num-iters-final <#iters|20> # Number of final iterations to give to nnet-combine-fast to "
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echo " # interpolate parameters (the weights are learned with a validation set)"
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echo " --mix-up <#mix|0> # If specified, add quasi-targets, analogous to a mixture of Gaussians vs."
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echo " # single Gaussians. Only do this if not already mixed-up."
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echo " --combine <true or false|true> # If true, do the final nnet-combine-fast stage."
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echo " --stage <stage|-5> # Used to run a partially-completed training process from somewhere in"
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echo " # the middle."
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exit 1;
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fi
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input_mdl=$1
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egs_dir=$2
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dir=$3
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# Check some files.
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for f in $input_mdl $egs_dir/egs.1.0.ark; do
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[ ! -f $f ] && echo "$0: expected file $f to exist." && exit 1;
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done
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mkdir -p $dir/log
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# Copy some things from the directory where the input model is located, to the
|
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# experimental directory, if they exist. These might be needed for things like
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# decoding.
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input_dir=$(dirname $input_mdl);
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for f in tree splice_opts cmvn_opts final.mat; do
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if [ -f $input_dir/$f ]; then
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cp $input_dir/$f $dir/
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fi
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done
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iters_per_epoch=$(cat $egs_dir/iters_per_epoch) || exit 1;
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num_jobs_nnet=$(cat $egs_dir/num_jobs_nnet) || exit 1;
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num_iters=$[$num_epochs * $iters_per_epoch];
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per_iter_learning_rate_factor=$(perl -e "print ($learning_rate_factor ** (1.0 / $num_iters));")
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|
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echo "$0: Will train for $num_epochs epochs, equalling $num_iters iterations."
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||||
|
||||
mix_up_iter=$[$num_iters/2]
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if [ $num_threads -eq 1 ]; then
|
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train_suffix="-simple" # this enables us to use GPU code if
|
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# we have just one thread.
|
||||
if ! cuda-compiled; then
|
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echo "$0: WARNING: you are running with one thread but you have not compiled"
|
||||
echo " for CUDA. You may be running a setup optimized for GPUs. If you have"
|
||||
echo " GPUs and have nvcc installed, go to src/ and do ./configure; make"
|
||||
fi
|
||||
else
|
||||
train_suffix="-parallel --num-threads=$num_threads"
|
||||
fi
|
||||
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||||
cp $input_mdl $dir/0.mdl || exit 1;
|
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|
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x=0
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|
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while [ $x -lt $num_iters ]; do
|
||||
if [ $x -ge 0 ] && [ $stage -le $x ]; then
|
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# Set off jobs doing some diagnostics, in the background.
|
||||
$cmd $dir/log/compute_prob_valid.$x.log \
|
||||
nnet-compute-prob $dir/$x.mdl ark:$egs_dir/valid_diagnostic.egs &
|
||||
$cmd $dir/log/compute_prob_train.$x.log \
|
||||
nnet-compute-prob $dir/$x.mdl ark:$egs_dir/train_diagnostic.egs &
|
||||
if [ $x -gt 0 ] && [ ! -f $dir/log/mix_up.$[$x-1].log ]; then
|
||||
$cmd $dir/log/progress.$x.log \
|
||||
nnet-show-progress --use-gpu=no $dir/$[$x-1].mdl $dir/$x.mdl ark:$egs_dir/train_diagnostic.egs &
|
||||
fi
|
||||
|
||||
echo "Training neural net (pass $x)"
|
||||
|
||||
|
||||
$cmd $parallel_opts JOB=1:$num_jobs_nnet $dir/log/train.$x.JOB.log \
|
||||
nnet-shuffle-egs --buffer-size=$shuffle_buffer_size --srand=$x \
|
||||
ark:$egs_dir/egs.JOB.$[$x%$iters_per_epoch].ark ark:- \| \
|
||||
nnet-train$train_suffix --minibatch-size=$minibatch_size --srand=$x $dir/$x.mdl \
|
||||
ark:- $dir/$[$x+1].JOB.mdl \
|
||||
|| exit 1;
|
||||
|
||||
nnets_list=
|
||||
for n in `seq 1 $num_jobs_nnet`; do
|
||||
nnets_list="$nnets_list $dir/$[$x+1].$n.mdl"
|
||||
done
|
||||
|
||||
$cmd $dir/log/average.$x.log \
|
||||
nnet-am-average $nnets_list - \| \
|
||||
nnet-am-copy --learning-rate-factor=$per_iter_learning_rate_factor - $dir/$[$x+1].mdl || exit 1;
|
||||
|
||||
if [ "$mix_up" -gt 0 ] && [ $x -eq $mix_up_iter ]; then
|
||||
# mix up.
|
||||
echo Mixing up from $num_leaves to $mix_up components
|
||||
$cmd $dir/log/mix_up.$x.log \
|
||||
nnet-am-mixup --min-count=10 --num-mixtures=$mix_up \
|
||||
$dir/$[$x+1].mdl $dir/$[$x+1].mdl || exit 1;
|
||||
fi
|
||||
rm $nnets_list
|
||||
fi
|
||||
x=$[$x+1]
|
||||
done
|
||||
|
||||
# Now do combination.
|
||||
# At the end, final.mdl will be a combination of the last e.g. 10 models.
|
||||
nnets_list=()
|
||||
[ $num_iters_final -gt $num_iters ] && num_iters_final=$num_iters
|
||||
[ "$mix_up" -gt 0 ] && [ $num_iters_final -gt $[$num_iters-$mix_up_iter] ] && \
|
||||
num_iters_final=$[$num_iters-$mix_up_iter]
|
||||
|
||||
start=$[$num_iters-$num_iters_final+1]
|
||||
for x in `seq $start $num_iters`; do
|
||||
idx=$[$x-$start]
|
||||
if [ $x -gt $mix_up_iter ]; then
|
||||
nnets_list[$idx]=$dir/$x.mdl # "nnet-am-copy --remove-dropout=true $dir/$x.mdl - |"
|
||||
fi
|
||||
done
|
||||
|
||||
if [ $stage -le $num_iters ]; then
|
||||
if $combine; then
|
||||
echo "Doing final combination to produce final.mdl"
|
||||
# Below, use --use-gpu=no to disable nnet-combine-fast from using a GPU, as
|
||||
# if there are many models it can give out-of-memory error; set num-threads to 8
|
||||
# to speed it up (this isn't ideal...)
|
||||
this_num_threads=$num_threads
|
||||
[ $this_num_threads -lt 8 ] && this_num_threads=8
|
||||
num_egs=`nnet-copy-egs ark:$egs_dir/combine.egs ark:/dev/null 2>&1 | tail -n 1 | awk '{print $NF}'`
|
||||
mb=$[($num_egs+$this_num_threads-1)/$this_num_threads]
|
||||
[ $mb -gt 512 ] && mb=512
|
||||
# Setting --initial-model to a large value makes it initialize the combination
|
||||
# with the average of all the models. It's important not to start with a
|
||||
# single model, or, due to the invariance to scaling that these nonlinearities
|
||||
# give us, we get zero diagonal entries in the fisher matrix that
|
||||
# nnet-combine-fast uses for scaling, which after flooring and inversion, has
|
||||
# the effect that the initial model chosen gets much higher learning rates
|
||||
# than the others. This prevents the optimization from working well.
|
||||
$cmd $parallel_opts $dir/log/combine.log \
|
||||
nnet-combine-fast --initial-model=100000 --num-lbfgs-iters=40 --use-gpu=no \
|
||||
--num-threads=$this_num_threads --regularizer=$combine_regularizer \
|
||||
--verbose=3 --minibatch-size=$mb "${nnets_list[@]}" ark:$egs_dir/combine.egs \
|
||||
$dir/final.mdl || exit 1;
|
||||
|
||||
# Normalize stddev for affine or block affine layers that are followed by a
|
||||
# pnorm layer and then a normalize layer.
|
||||
$cmd $parallel_opts $dir/log/normalize.log \
|
||||
nnet-normalize-stddev $dir/final.mdl $dir/final.mdl || exit 1;
|
||||
|
||||
# Compute the probability of the final, combined model with
|
||||
# the same subset we used for the previous compute_probs, as the
|
||||
# different subsets will lead to different probs.
|
||||
$cmd $dir/log/compute_prob_valid.final.log \
|
||||
nnet-compute-prob $dir/final.mdl ark:$egs_dir/valid_diagnostic.egs &
|
||||
$cmd $dir/log/compute_prob_train.final.log \
|
||||
nnet-compute-prob $dir/final.mdl ark:$egs_dir/train_diagnostic.egs &
|
||||
else
|
||||
echo "$0: --combine=false so just using last model."
|
||||
cp $dir/$x.mdl $dir/final.mdl
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le $[$num_iters+1] ]; then
|
||||
echo "Getting average posterior for purposes of adjusting the priors."
|
||||
# Note: this just uses CPUs, using a smallish subset of data.
|
||||
rm $dir/post.*.vec 2>/dev/null
|
||||
$cmd JOB=1:$num_jobs_nnet $dir/log/get_post.JOB.log \
|
||||
nnet-subset-egs --n=$prior_subset_size ark:$egs_dir/egs.JOB.0.ark ark:- \| \
|
||||
nnet-compute-from-egs "nnet-to-raw-nnet $dir/final.mdl -|" ark:- ark:- \| \
|
||||
matrix-sum-rows ark:- ark:- \| vector-sum ark:- $dir/post.JOB.vec || exit 1;
|
||||
|
||||
sleep 3; # make sure there is time for $dir/post.*.vec to appear.
|
||||
|
||||
$cmd $dir/log/vector_sum.log \
|
||||
vector-sum $dir/post.*.vec $dir/post.vec || exit 1;
|
||||
|
||||
rm $dir/post.*.vec;
|
||||
|
||||
echo "Re-adjusting priors based on computed posteriors"
|
||||
$cmd $dir/log/adjust_priors.log \
|
||||
nnet-adjust-priors $dir/final.mdl $dir/post.vec $dir/final.mdl || exit 1;
|
||||
fi
|
||||
|
||||
|
||||
sleep 2
|
||||
|
||||
echo Done
|
||||
|
||||
|
||||
$remove_egs && steps/nnet2/remove_egs.sh $dir/egs
|
||||
|
||||
if $cleanup; then
|
||||
echo Removing most of the models
|
||||
for x in `seq 0 $num_iters`; do
|
||||
if [ $[$x%100] -ne 0 ] && [ $x -lt $[$num_iters-$num_iters_final+1] ]; then
|
||||
# delete all but every 100th model; don't delete the ones which combine to form the final model.
|
||||
rm $dir/$x.mdl
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
|
@ -0,0 +1 @@
|
|||
nnet-init
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Make sure we are in the tools/ directory.
|
||||
if [ `basename $PWD` == extras ]; then
|
||||
cd ..
|
||||
fi
|
||||
|
||||
! [ `basename $PWD` == tools ] && \
|
||||
echo "You must call this script from the tools/ directory" && exit 1;
|
||||
|
||||
echo "Installing RNNLM-HS 0.1b"
|
||||
|
||||
cd rnnlm-hs-0.1b
|
||||
make
|
||||
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