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egs/wsj/s5/local/nnet2/run_pnorm_bnf.sh
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#!/bin/bash # Note: In order to run BNF, run run_bnf.sh use_gpu=true bnf_train_stage=-100 . ./path.sh . ./cmd.sh . utils/parse_options.sh set -e set -o pipefail set -u 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. EOF fi parallel_opts="--gpu 1" num_threads=1 minibatch_size=512 dir=exp_bnf/tri6_bnf_gpu 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 parallel_opts="--num-threads $num_threads" minibatch_size=128 dir=exp_bnf/tri6_bnf fi align_dir=exp/tri4b_ali_si284 mkdir -p exp_bnf mkdir -p $dir if [ ! -f $dir/.done ]; then echo --------------------------------------------------------------------- echo "Starting training the bottleneck network" echo --------------------------------------------------------------------- steps/nnet2/train_pnorm_bottleneck_fast.sh \ --samples-per-iter 400000 \ --stage $bnf_train_stage --num-jobs-nnet 4 \ --parallel-opts "$parallel_opts" \ --num-threads "$num_threads" \ --minibatch-size "$minibatch_size" \ --initial-learning-rate 0.02 --final-learning-rate 0.004 \ --pnorm-input-dim 2000 --pnorm-output-dim 400 \ --num-jobs-nnet 8 --mix-up 8000 \ --num-hidden-layers 4 \ --bottleneck-dim 42 --cmd "$train_cmd" \ data/train_si284 data/lang $align_dir $dir || exit 1 touch $dir/.done fi [ ! -d param_bnf ] && mkdir -p param_bnf if [ ! -f data_bnf/train_bnf/.done ]; then mkdir -p data_bnf # put the archives in param_bnf/. steps/nnet2/dump_bottleneck_features.sh --cmd "$train_cmd" \ --transform-dir exp/tri4a data/train_si284 data_bnf/train_bnf $dir param_bnf exp_bnf/dump_bnf touch data_bnf/train_bnf/.done fi [ ! -d data/test_eval92 ] && echo "No such directory data/test_eval92" && exit 1; [ ! -d data/test_dev93 ] && echo "No such directory data/test_dev93" && exit 1; [ ! -d exp/tri4b/decode_bd_tgpr_eval92 ] && echo "No such directory exp/tri4b/decode_bd_tgpr_eval92" && exit 1; [ ! -d exp/tri4b/decode_bd_tgpr_dev93 ] && echo "No such directory exp/tri4b/decode_bd_tgpr_dev93" && exit 1; # put the archives in param_bnf/. steps/nnet2/dump_bottleneck_features.sh --nj 8 \ --transform-dir exp/tri4b/decode_bd_tgpr_eval92 data/test_eval92 data_bnf/eval92_bnf $dir param_bnf exp_bnf/dump_bnf steps/nnet2/dump_bottleneck_features.sh --nj 10 \ --transform-dir exp/tri4b/decode_bd_tgpr_dev93 data/test_dev93 data_bnf/dev93_bnf $dir param_bnf exp_bnf/dump_bnf if [ ! data_bnf/train/.done -nt data_bnf/train_bnf/.done ]; then steps/nnet/make_fmllr_feats.sh --cmd "$train_cmd --max-jobs-run 10" \ --transform-dir $align_dir data_bnf/train_sat data/train_si284 \ exp/tri4b exp_bnf/make_fmllr_feats/log param_bnf/ steps/append_feats.sh --cmd "$train_cmd" --nj 4 \ data_bnf/train_bnf data_bnf/train_sat data_bnf/train \ exp_bnf/append_feats/log param_bnf/ steps/compute_cmvn_stats.sh --fake data_bnf/train exp_bnf/make_fmllr_feats param_bnf rm -r data_bnf/train_sat touch data_bnf/train/.done fi ## preparing Bottleneck features for eval92 and dev93 steps/nnet/make_fmllr_feats.sh \ --nj 8 --transform-dir exp/tri4b/decode_bd_tgpr_eval92 data_bnf/eval92_sat data/test_eval92 \ $align_dir exp_bnf/make_fmllr_feats/log param_bnf/ steps/nnet/make_fmllr_feats.sh \ --nj 10 --transform-dir exp/tri4b/decode_bd_tgpr_dev93 data_bnf/dev93_sat data/test_dev93 \ $align_dir exp_bnf/make_fmllr_feats/log param_bnf/ steps/append_feats.sh --nj 4 \ data_bnf/eval92_bnf data_bnf/eval92_sat data_bnf/eval92 \ exp_bnf/append_feats/log param_bnf/ steps/append_feats.sh --nj 4 \ data_bnf/dev93_bnf data_bnf/dev93_sat data_bnf/dev93 \ exp_bnf/append_feats/log param_bnf/ steps/compute_cmvn_stats.sh --fake data_bnf/eval92 exp_bnf/make_fmllr_feats param_bnf steps/compute_cmvn_stats.sh --fake data_bnf/dev93 exp_bnf/make_fmllr_feats param_bnf rm -r data_bnf/eval92_sat rm -r data_bnf/dev93_sat exit 0; |