Yannick Estève / ONTRAC-Kaldi

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egs/gale_mandarin/s5/run.sh 7.67 KB
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8dcb6dfcb   Yannick Estève   first commit 
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  #!/bin/bash
  
  # Copyright 2014 (author: Hainan Xu, Ahmed Ali)
  # Apache 2.0
  
  . ./path.sh
  . ./cmd.sh
  
  num_jobs=64
  num_jobs_decode=128
  
  AUDIO=(
    /export/corpora/LDC/LDC2013S08/
    /export/corpora/LDC/LDC2013S04/
    /export/corpora/LDC/LDC2014S09/
    /export/corpora/LDC/LDC2015S06/
    /export/corpora/LDC/LDC2015S13/
    /export/corpora/LDC/LDC2016S03/
  )
  TEXT=(
    /export/corpora/LDC/LDC2013T20/
    /export/corpora/LDC/LDC2013T08/
    /export/corpora/LDC/LDC2014T28/
    /export/corpora/LDC/LDC2015T09/
    /export/corpora/LDC/LDC2015T25/
    /export/corpora/LDC/LDC2016T12/
  )
  galeData=GALE/
  
  # You can run the script from here automatically, but it is recommended to run the data preparation,
  # and features extraction manually and and only once.
  # By copying and pasting into the shell.
  
  set -e -o pipefail
  set -x
  
  local/gale_data_prep_audio.sh "${AUDIO[@]}" $galeData
  
  local/gale_data_prep_txt.sh  "${TEXT[@]}" $galeData
  
  local/gale_data_prep_split.sh $galeData
  local/gale_prep_dict.sh
  
  utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang
  
  local/gale_train_lms.sh
  
  local/gale_format_data.sh
  
  # Now make MFCC features.
  # mfccdir should be some place with a largish disk where you
  # want to store MFCC features.
  mfccdir=mfcc
  
  # spread the mfccs over various machines, as this data-set is quite large.
  if [[  $(hostname -f) ==  *.clsp.jhu.edu ]]; then
    mfcc=$(basename $mfccdir) # in case was absolute pathname (unlikely), get basename.
    utils/create_split_dir.pl /export/b{05,06,07,08}/$USER/kaldi-data/egs/gale_mandarin/s5/$mfcc/storage \
      $mfccdir/storage
  fi
  
  for x in train dev ; do
    utils/fix_data_dir.sh data/$x
    steps/make_mfcc_pitch.sh --cmd "$train_cmd" --nj $num_jobs \
      data/$x exp/make_mfcc/$x $mfccdir
    utils/fix_data_dir.sh data/$x # some files fail to get mfcc for many reasons
    steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
  done
  
  # Let's create a subset with 10k segments to make quick flat-start training:
  utils/subset_data_dir.sh data/train 10000 data/train.10k || exit 1;
  utils/subset_data_dir.sh data/train 50000 data/train.50k || exit 1;
  utils/subset_data_dir.sh data/train 100000 data/train.100k || exit 1;
  
  # Train monophone models on a subset of the data, 10K segment
  # Note: the --boost-silence option should probably be omitted by default
  steps/train_mono.sh --nj 40 --cmd "$train_cmd" \
    data/train.10k data/lang exp/mono || exit 1;
  
  # Get alignments from monophone system.
  steps/align_si.sh --nj $num_jobs --cmd "$train_cmd" \
    data/train.50k data/lang exp/mono exp/mono_ali.50k || exit 1;
  
  # train tri1 [first triphone pass]
  steps/train_deltas.sh --cmd "$train_cmd" \
    2500 30000 data/train.50k data/lang exp/mono_ali.50k exp/tri1 || exit 1;
  
  # First triphone decoding
  utils/mkgraph.sh data/lang_test exp/tri1 exp/tri1/graph || exit 1;
  steps/decode.sh  --nj $num_jobs_decode --cmd "$decode_cmd" \
    exp/tri1/graph data/dev exp/tri1/decode &
  
  steps/align_si.sh --nj $num_jobs --cmd "$train_cmd" \
    data/train data/lang exp/tri1 exp/tri1_ali || exit 1;
  
  # Train tri2a, which is deltas+delta+deltas
  steps/train_deltas.sh --cmd "$train_cmd" \
    3000 40000 data/train data/lang exp/tri1_ali exp/tri2a || exit 1;
  
  # tri2a decoding
  utils/mkgraph.sh data/lang_test exp/tri2a exp/tri2a/graph || exit 1;
  steps/decode.sh --nj $num_jobs_decode --cmd "$decode_cmd" \
    exp/tri2a/graph data/dev exp/tri2a/decode &
  
  steps/align_si.sh --nj $num_jobs --cmd "$train_cmd" \
    data/train data/lang exp/tri2a exp/tri2a_ali || exit 1;
  
  # train and decode tri2b [LDA+MLLT]
  steps/train_lda_mllt.sh --cmd "$train_cmd" 4000 50000 \
    data/train data/lang exp/tri2a_ali exp/tri2b || exit 1;
  utils/mkgraph.sh data/lang_test exp/tri2b exp/tri2b/graph || exit 1;
  steps/decode.sh --nj $num_jobs_decode --cmd "$decode_cmd" exp/tri2b/graph data/dev exp/tri2b/decode &
  
  # Align all data with LDA+MLLT system (tri2b)
  steps/align_si.sh --nj $num_jobs --cmd "$train_cmd" \
    --use-graphs true data/train data/lang exp/tri2b exp/tri2b_ali  || exit 1;
  
  #  Do MMI on top of LDA+MLLT.
  steps/make_denlats.sh --nj $num_jobs --cmd "$train_cmd" \
   data/train data/lang exp/tri2b exp/tri2b_denlats || exit 1;
  
  steps/train_mmi.sh data/train data/lang exp/tri2b_ali \
   exp/tri2b_denlats exp/tri2b_mmi
  
  steps/decode.sh  --iter 4 --nj $num_jobs --cmd "$decode_cmd"  exp/tri2b/graph \
   data/dev exp/tri2b_mmi/decode_it4 &
  steps/decode.sh  --iter 3 --nj $num_jobs --cmd "$decode_cmd" exp/tri2b/graph \
   data/dev exp/tri2b_mmi/decode_it3 & # Do the same with boosting.
  
  steps/train_mmi.sh --boost 0.1 data/train data/lang exp/tri2b_ali \
  exp/tri2b_denlats exp/tri2b_mmi_b0.1
  
  steps/decode.sh  --iter 4 --nj $num_jobs --cmd "$decode_cmd" exp/tri2b/graph \
   data/dev exp/tri2b_mmi_b0.1/decode_it4 &
  steps/decode.sh  --iter 3 --nj $num_jobs --cmd "$decode_cmd" exp/tri2b/graph \
   data/dev exp/tri2b_mmi_b0.1/decode_it3 &
  
  # Do MPE.
  steps/train_mpe.sh data/train data/lang exp/tri2b_ali exp/tri2b_denlats exp/tri2b_mpe || exit 1;
  
  steps/decode.sh  --iter 4 --nj $num_jobs_decode --cmd "$decode_cmd" exp/tri2b/graph \
   data/dev exp/tri2b_mpe/decode_it4 &
  
  steps/decode.sh  --iter 3 --nj $num_jobs_decode --cmd "$decode_cmd" exp/tri2b/graph \
   data/dev exp/tri2b_mpe/decode_it3 &
  
  # From 2b system, train 3b which is LDA + MLLT + SAT.
  steps/train_sat.sh --cmd "$train_cmd" \
    5000 100000 data/train data/lang exp/tri2b_ali exp/tri3b || exit 1;
  utils/mkgraph.sh data/lang_test exp/tri3b exp/tri3b/graph|| exit 1;
  steps/decode_fmllr.sh --nj $num_jobs_decode --cmd "$decode_cmd" \
    exp/tri3b/graph data/dev exp/tri3b/decode &
  
  # From 3b system, align all data.
  steps/align_fmllr.sh --nj $num_jobs --cmd "$train_cmd" \
    data/train data/lang exp/tri3b exp/tri3b_ali || exit 1;
  
  ## SGMM (subspace gaussian mixture model), excluding the "speaker-dependent weights"
  steps/train_ubm.sh --cmd "$train_cmd" 700 \
   data/train data/lang exp/tri3b_ali exp/ubm5a || exit 1;
  
  steps/train_sgmm2.sh --cmd "$train_cmd" 5000 20000 data/train data/lang exp/tri3b_ali \
    exp/ubm5a/final.ubm exp/sgmm_5a || exit 1;
  
  utils/mkgraph.sh data/lang_test exp/sgmm_5a exp/sgmm_5a/graph || exit 1;
  steps/decode_sgmm2.sh --nj $num_jobs_decode --cmd "$decode_cmd" --config conf/decode.config \
    --transform-dir exp/tri3b/decode exp/sgmm_5a/graph data/dev exp/sgmm_5a/decode &
  
  steps/align_sgmm2.sh --nj $num_jobs --cmd "$train_cmd" --transform-dir exp/tri3b_ali \
    --use-graphs true --use-gselect true data/train data/lang exp/sgmm_5a exp/sgmm_5a_ali || exit 1;
  
  ## boosted MMI on SGMM
  steps/make_denlats_sgmm2.sh --nj $num_jobs --sub-split $num_jobs --beam 9.0 --lattice-beam 6 \
    --cmd "$decode_cmd" --num-threads 4 --transform-dir exp/tri3b_ali \
    data/train data/lang exp/sgmm_5a_ali exp/sgmm_5a_denlats || exit 1;
  
  steps/train_mmi_sgmm2.sh --cmd "$train_cmd" --num-iters 8 --transform-dir exp/tri3b_ali --boost 0.1 \
    data/train data/lang exp/sgmm_5a exp/sgmm_5a_denlats exp/sgmm_5a_mmi_b0.1
  
  #decode GMM MMI
  utils/mkgraph.sh data/lang_test exp/sgmm_5a_mmi_b0.1 exp/sgmm_5a_mmi_b0.1/graph || exit 1;
  
  steps/decode_sgmm2.sh --nj $num_jobs_decode --cmd "$decode_cmd" --config conf/decode.config \
    --transform-dir exp/tri3b/decode exp/sgmm_5a_mmi_b0.1/graph data/dev exp/sgmm_5a_mmi_b0.1/decode
  
  for n in 1 2 3 4; do
    steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $n --transform-dir exp/tri3b/decode data/lang_test \
      data/dev exp/sgmm_5a_mmi_b0.1/decode exp/sgmm_5a_mmi_b0.1/decode$n
  done
  
  wait
  #local/nnet/run_dnn.sh
  
  echo "# Get WER and CER" > RESULTS
  for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_[0-9]* | utils/best_wer.sh; \
  done | sort -n -r -k2 >> RESULTS
  echo "" >> RESULTS
  for x in exp/*/decode*; do [ -d $x ] && grep WER $x/cer_[0-9]* | utils/best_wer.sh; \
  done | sort -n -r -k2 >> RESULTS
  
  echo -e "
  # Detailed WER on all corpus dev sets" >> RESULTS
  local/split_wer_per_corpus.sh $galeData >> RESULTS
  
  echo training succedded
  exit 0