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egs/rm/s5/local/nnet2/run_5e_gpu.sh
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#!/bin/bash
# This script demonstrates discriminative training of ensemble-trained p-norm neural nets.
# It's on top of run_4e_gpu.sh which uses adapted 40-dimensional features.
# This version of the script uses GPUs. We distinguish it by putting "_gpu"
# at the end of the directory name.
gpu_opts="--gpu 1" # This is suitable for the CLSP network,
# you'll likely have to change it. we'll
# use it later on, in the training (it's
# not used in denlat creation)
. ./cmd.sh
. ./path.sh
! cuda-compiled && 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
# The denominator lattice creation currently doesn't use GPUs.
# Note: we specify 1G for --mem, which is per
# thread... it will likely be less than the default. 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=$(cat exp/tri3b_ali/num_jobs)
dir=nnet4e_gpu
steps/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G" \
--nj $nj --sub-split 20 --num-threads 6 --parallel-opts "--num-threads 6" \
--beam 20.0 --lattice-beam 10.0 \
--transform-dir exp/tri3b_ali \
data/train data/lang exp/$dir exp/$dir_denlats
steps/nnet2/align.sh --cmd "$decode_cmd $gpu_opts" --use-gpu yes \
--transform-dir exp/tri3b_ali \
--nj $nj data/train data/lang exp/$dir exp/$dir_ali
steps/nnet2/train_discriminative.sh --cmd "$decode_cmd" \
--num-jobs-nnet 2 --transform-dir exp/tri3b_ali \
--num-threads 1 --parallel-opts "$gpu_opts" data/train data/lang \
exp/$dir_ali exp/$dir_denlats exp/$dir/final.mdl exp/nnet5e_mpe_gpu
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/nnet5e_mpe_gpu/decode_epoch$epoch &
steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 --iter epoch$epoch \
--transform-dir exp/tri3b/decode_ug \
exp/tri3b/graph_ug data/test exp/nnet5e_mpe_gpu/decode_ug_epoch$epoch &
done
exit 0;
# The following is some test commands that I ran in order to verify that
# the neural-net splitting and excising code was working as intended.
# (
# acoustic_scale=0.1
# for criterion in smbr mmi mpfe; do
# for drop_frames in true false; do
# nnet-get-egs-discriminative --drop-frames=$drop_frames --criterion=$criterion --excise=true exp/tri5c_mpe/0.mdl 'ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:data/train/split8/1/utt2spk scp:data/train/split8/1/cmvn.scp "scp:head -n 40 data/train/split8/1/feats.scp|" ark:- | splice-feats --left-context=3 --right-context=3 ark:- ark:- | transform-feats exp/tri5c_mpe/final.mat ark:- ark:- | transform-feats --utt2spk=ark:data/train/split8/1/utt2spk ark:exp/tri3b_ali/trans.1 ark:- ark:- |' 'ark,s,cs:gunzip -c exp/$dir_ali/ali.1.gz |' 'ark,s,cs:gunzip -c exp/$dir_denlats/lat.1.gz|' "ark:|nnet-combine-egs-discriminative ark:- ark:1.egs"
# nnet-get-egs-discriminative --drop-frames=$drop_frames --criterion=$criterion --split=false --excise=false exp/tri5c_mpe/0.mdl 'ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:data/train/split8/1/utt2spk scp:data/train/split8/1/cmvn.scp "scp:head -n 40 data/train/split8/1/feats.scp|" ark:- | splice-feats --left-context=3 --right-context=3 ark:- ark:- | transform-feats exp/tri5c_mpe/final.mat ark:- ark:- | transform-feats --utt2spk=ark:data/train/split8/1/utt2spk ark:exp/tri3b_ali/trans.1 ark:- ark:- |' 'ark,s,cs:gunzip -c exp/$dir_ali/ali.1.gz |' 'ark,s,cs:gunzip -c exp/$dir_denlats/lat.1.gz|' ark:2.egs
# nnet-compare-hash-discriminative --acoustic-scale=$acoustic_scale --drop-frames=$drop_frames --criterion=$criterion exp/$dir/final.mdl ark:1.egs ark:2.egs || exit 1;
# done
# done
# )
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