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egs/rm/s5/local/nnet2/run_4b_gpu.sh 3.12 KB
8dcb6dfcb   Yannick Estève   first commit
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  #!/bin/bash
  
  
  stage=0
  train_stage=-100
  # This trains only unadapted (just cepstral mean normalized) features,
  # and uses various combinations of VTLN warping factor and time-warping
  # factor to artificially expand the amount of data.
  
  
  . ./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
  
  parallel_opts="--gpu 1"  # This is suitable for the CLSP network, you'll likely have to change it.
  
  . utils/parse_options.sh  # to parse the --stage option, if given
  
  [ $# != 0 ] && echo "Usage: local/run_4b.sh [--stage <stage> --train-stage <train-stage>]" && exit 1;
  
  
  set -e
  
  if [ $stage -le 0 ]; then
    # Create the training data.                                                                                                                   
    featdir=`pwd`/mfcc/nnet4b; mkdir -p $featdir
    fbank_conf=conf/fbank_40.conf
    echo "--num-mel-bins=40" > $fbank_conf
    steps/nnet2/get_perturbed_feats.sh --cmd "$train_cmd" \
      $fbank_conf $featdir exp/perturbed_fbanks data/train data/train_perturbed_fbank &
    steps/nnet2/get_perturbed_feats.sh --cmd "$train_cmd" --feature-type mfcc \
      conf/mfcc.conf $featdir exp/perturbed_mfcc data/train data/train_perturbed_mfcc &
    wait
  fi
  
  if [ $stage -le 1 ]; then
    steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
      data/train_perturbed_mfcc data/lang exp/tri3b exp/tri3b_ali_perturbed_mfcc
  fi
  
  
  if [ $stage -le 2 ]; then
    steps/nnet2/train_block.sh --stage "$train_stage" \
       --num-jobs-nnet 4 --num-threads 1 --parallel-opts "$parallel_opts" \
       --bias-stddev 0.5 --splice-width 7 --egs-opts "--feat-type raw" \
       --softmax-learning-rate-factor 0.5 \
       --initial-learning-rate 0.04 --final-learning-rate 0.004 \
       --num-epochs-extra 10 --add-layers-period 3 --mix-up 4000 \
       --cmd "$decode_cmd" --hidden-layer-dim 450 \
        data/train_perturbed_fbank data/lang exp/tri3b_ali_perturbed_mfcc exp/nnet4b_gpu  || exit 1
  fi
  
  
  if [ $stage -le 3 ]; then
    # Create the testing data.
    featdir=`pwd`/mfcc
    mkdir -p $featdir
    fbank_conf=conf/fbank_40.conf
    echo "--num-mel-bins=40" > $fbank_conf
    for x in test_mar87 test_oct87 test_feb89 test_oct89 test_feb91 test_sep92 train; do
      mkdir -p data/${x}_fbank
      cp data/$x/* data/${x}_fbank || true
      steps/make_fbank.sh --fbank-config "$fbank_conf" --nj 8 \
        --cmd "run.pl" data/${x}_fbank exp/make_fbank/$x $featdir  || exit 1;
      steps/compute_cmvn_stats.sh data/${x}_fbank exp/make_fbank/$x $featdir  || exit 1;
    done
    utils/combine_data.sh data/test_fbank data/test_{mar87,oct87,feb89,oct89,feb91,sep92}_fbank
    steps/compute_cmvn_stats.sh data/test_fbank exp/make_fbank/test $featdir  
  fi
  
  if [ $stage -le 4 ]; then
     steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 --feat-type raw \
       exp/tri3b/graph data/test_fbank exp/nnet4b_gpu/decode
     steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 --feat-type raw \
       exp/tri3b/graph_ug data/test_fbank exp/nnet4b_gpu/decode_ug
  fi