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egs/rm/s5/local/nnet2/run_5c.sh
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#!/bin/bash # This is neural net training on top of adapted 40-dimensional features. # This version of the script uses GPUs. We distinguish it by putting "_gpu" # at the end of the directory name. # # Since we're using one quarter the number of jobs (num-jobs-nnet) as the # run_4c.sh script, we halve the learning rate (generally speaking, splitting # the difference like this is probably a good idea.) stage=0 train_stage=-100 . ./cmd.sh || exit 1; . utils/parse_options.sh || exit 1; # We increase the beam relative to the defaults; this is just for this RM setup, # where the default beams will likely generate very thin lattices. Note: the # transform-dir is important to specify, since this system is on top of fMLLR # features. nj=8 if [ $stage -le 0 ]; then steps/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G" \ --nj $nj --sub-split 20 --num-threads 6 --parallel-opts "--num-threads 6" \ --beam 20.0 --lattice-beam 10.0 \ --transform-dir exp/tri3b_ali \ data/train data/lang exp/nnet4c exp/nnet4c_denlats fi if [ $stage -le 1 ]; then steps/nnet2/align.sh --cmd "$decode_cmd --mem 1G" \ --transform-dir exp/tri3b_ali \ --nj $nj data/train data/lang exp/nnet4c exp/nnet4c_ali fi if [ $stage -le 2 ]; then steps/nnet2/train_discriminative.sh --cmd "$decode_cmd" \ --stage $train_stage \ --transform-dir exp/tri3b_ali \ --num-jobs-nnet 2 data/train data/lang \ exp/nnet4c_ali exp/nnet4c_denlats exp/nnet4c/final.mdl exp/nnet5c_mpe fi if [ $stage -le 3 ]; then for epoch in 1 2 3 4; do steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 --iter epoch$epoch \ --transform-dir exp/tri3b/decode \ exp/tri3b/graph data/test exp/nnet5c_mpe/decode_epoch$epoch & steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 --iter epoch$epoch \ --transform-dir exp/tri3b/decode_ug \ exp/tri3b/graph_ug data/test exp/nnet5c_mpe/decode_ug_epoch$epoch & done fi |