Yannick Estève / ONTRAC-Kaldi

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egs/callhome_egyptian/s5/run.sh 7.08 KB
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8dcb6dfcb   Yannick Estève   first commit 
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  #!/bin/bash
  #
  # Johns Hopkins University (Author : Gaurav Kumar, Daniel Povey)
  # Recipe for CallHome Egyptian Arabic
  # Made to integrate KALDI with JOSHUA for end-to-end ASR and SMT
  
  . ./cmd.sh
  . ./path.sh
  mfccdir=`pwd`/mfcc
  set -e
  
  # Specify the location of the speech files, the transcripts and the lexicon
  # These are passed off to other scripts in including the one for data and lexicon prep
  
  eca_speech=/export/corpora/LDC/LDC97S45
  eca_transcripts=/export/corpora/LDC/LDC97T19
  eca_lexicon=/export/corpora/LDC/LDC99L22
  sup_speech=/export/corpora/LDC/LDC2002S37
  sup_transcripts=/export/corpora/LDC/LDC2002T38
  h5_speech=/export/corpora/LDC/LDC2002S22
  h5_transcripts=/export/corpora/LDC/LDC2002T39
  split=local/splits
  
  local/callhome_data_prep.sh $eca_speech $eca_transcripts $sup_speech $sup_transcripts $h5_speech $h5_transcripts
  
  local/callhome_prepare_dict.sh $eca_lexicon
  
  # Added c,j, v to the non silences phones manually
  utils/prepare_lang.sh data/local/dict "<unk>" data/local/lang data/lang
  
  # Make sure that you do not use your test and your dev sets to train the LM
  # Some form of cross validation is possible where you decode your dev/set based on an
  # LM that is trained on  everything but that that conversation
  local/callhome_train_lms.sh $split
  local/callhome_create_test_lang.sh
  
  utils/fix_data_dir.sh data/local/data/train_all
  
  steps/make_mfcc.sh --nj 20 --cmd "$train_cmd" data/local/data/train_all exp/make_mfcc/train_all $mfccdir || exit 1;
  
  utils/fix_data_dir.sh data/local/data/train_all
  utils/validate_data_dir.sh data/local/data/train_all
  
  cp -r data/local/data/train_all data/train_all
  
  # Creating data partitions for the pipeline
  
  local/create_splits $split
  
  # Now compute CMVN stats for the train, dev and test subsets
  steps/compute_cmvn_stats.sh data/dev exp/make_mfcc/dev $mfccdir
  steps/compute_cmvn_stats.sh data/test exp/make_mfcc/test $mfccdir
  steps/compute_cmvn_stats.sh data/sup exp/make_mfcc/sup $mfccdir
  steps/compute_cmvn_stats.sh data/h5 exp/make_mfcc/h5 $mfccdir
  
  steps/compute_cmvn_stats.sh data/train exp/make_mfcc/train $mfccdir
  
  # Again from Dan's recipe : Reduced monophone training data
  # Now-- there are 1.6 million utterances, and we want to start the monophone training
  # on relatively short utterances (easier to align), but not only the very shortest
  # ones (mostly uh-huh).  So take the 100k shortest ones, and then take 10k random
  # utterances from those.
  
  steps/train_mono.sh --nj 10 --cmd "$train_cmd" \
    data/train data/lang exp/mono0a
  
  steps/align_si.sh --nj 30 --cmd "$train_cmd" \
     data/train data/lang exp/mono0a exp/mono0a_ali || exit 1;
  
  steps/train_deltas.sh --cmd "$train_cmd" \
      1000 10000 data/train data/lang exp/mono0a_ali exp/tri1 || exit 1;
  
  
  (utils/mkgraph.sh data/lang_test exp/tri1 exp/tri1/graph
   steps/decode.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
     exp/tri1/graph data/dev exp/tri1/decode_dev)&
  
  steps/align_si.sh --nj 30 --cmd "$train_cmd" \
     data/train data/lang exp/tri1 exp/tri1_ali || exit 1;
  
  steps/train_deltas.sh --cmd "$train_cmd" \
      1400 15000 data/train data/lang exp/tri1_ali exp/tri2 || exit 1;
  
  (
    utils/mkgraph.sh data/lang_test exp/tri2 exp/tri2/graph || exit 1;
    steps/decode.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
     exp/tri2/graph data/dev exp/tri2/decode_dev || exit 1;
  )&
  
  steps/align_si.sh --nj 30 --cmd "$train_cmd" \
    data/train data/lang exp/tri2 exp/tri2_ali || exit 1;
  
  # Train tri3a, which is LDA+MLLT, on 100k data.
  steps/train_lda_mllt.sh --cmd "$train_cmd" \
     --splice-opts "--left-context=3 --right-context=3" \
     1800 20000 data/train data/lang exp/tri2_ali exp/tri3a || exit 1;
  (
    utils/mkgraph.sh data/lang_test exp/tri3a exp/tri3a/graph || exit 1;
    steps/decode.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
     exp/tri3a/graph data/dev exp/tri3a/decode_dev || exit 1;
  )&
  
  # Next we'll use fMLLR and train with SAT (i.e. on
  # fMLLR features)
  
  steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
    data/train data/lang exp/tri3a exp/tri3a_ali || exit 1;
  
  steps/train_sat.sh  --cmd "$train_cmd" \
    2200 25000 data/train data/lang exp/tri3a_ali  exp/tri4a || exit 1;
  
  (
    utils/mkgraph.sh data/lang_test exp/tri4a exp/tri4a/graph
    steps/decode_fmllr.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
     exp/tri4a/graph data/dev exp/tri4a/decode_dev
  )&
  
  
  steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
    data/train data/lang exp/tri4a exp/tri4a_ali || exit 1;
  
  # Reduce the number of gaussians
  steps/train_sat.sh  --cmd "$train_cmd" \
    2600 30000 data/train data/lang exp/tri4a_ali  exp/tri5a || exit 1;
  
  (
    utils/mkgraph.sh data/lang_test exp/tri5a exp/tri5a/graph
    steps/decode_fmllr.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
     exp/tri5a/graph data/dev exp/tri5a/decode_dev
  )&
  
  (
    steps/decode_fmllr.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
    exp/tri5a/graph data/test exp/tri5a/decode_test
    # Decode Supplement and H5
    steps/decode_fmllr.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
    exp/tri5a/graph data/sup exp/tri5a/decode_sup
    steps/decode_fmllr.sh --nj 25 --cmd "$decode_cmd" --config conf/decode.config \
    exp/tri5a/graph data/h5 exp/tri5a/decode_h5
  )&
  
  dnn_cpu_parallel_opts=(--minibatch-size 128 --max-change 10 --num-jobs-nnet 8 --num-threads 16 \
                         --parallel-opts "--num-threads 16" --cmd "queue.pl  --mem 1G")
  dnn_gpu_parallel_opts=(--minibatch-size 512 --max-change 40 --num-jobs-nnet 4 --num-threads 1 \
                         --parallel-opts "--gpu 1" --cmd "queue.pl  --mem 1G")
  
  steps/nnet2/train_pnorm_ensemble.sh \
    --mix-up 5000  --initial-learning-rate 0.008 --final-learning-rate 0.0008\
    --num-hidden-layers 4 --pnorm-input-dim 2000 --pnorm-output-dim 200\
    --cmd "$train_cmd" \
    "${dnn_gpu_parallel_opts[@]}" \
    --ensemble-size 4 --initial-beta 0.1 --final-beta 5 \
    data/train data/lang exp/tri5a_ali exp/tri6a_dnn
  
  (
    steps/nnet2/decode.sh --nj 13 --cmd "$decode_cmd" --num-threads 4 --parallel-opts " --num-threads 4"   \
      --scoring-opts "--min-lmwt 8 --max-lmwt 16" --transform-dir exp/tri5a/decode_dev exp/tri5a/graph data/dev exp/tri6a_dnn/decode_dev
  ) &
  
  # Decode test sets
  (
    steps/nnet2/decode.sh --nj 13 --cmd "$decode_cmd" --num-threads 4 --parallel-opts " --num-threads 4"   \
      --scoring-opts "--min-lmwt 8 --max-lmwt 16" --transform-dir exp/tri5a/decode_test exp/tri5a/graph data/test exp/tri6a_dnn/decode_test
    steps/nnet2/decode.sh --nj 13 --cmd "$decode_cmd" --num-threads 4 --parallel-opts " --num-threads 4"   \
      --scoring-opts "--min-lmwt 8 --max-lmwt 16" --transform-dir exp/tri5a/decode_sup exp/tri5a/graph data/sup exp/tri6a_dnn/decode_sup
    steps/nnet2/decode.sh --nj 13 --cmd "$decode_cmd" --num-threads 4 --parallel-opts " --num-threads 4"   \
      --scoring-opts "--min-lmwt 8 --max-lmwt 16" --transform-dir exp/tri5a/decode_h5 exp/tri5a/graph data/h5 exp/tri6a_dnn/decode_h5
  ) &
  
  wait
  
  # (TDNN + iVectors) training
  # Note that the alignments used by run_tdnn.sh come from the pnorm-ensemble model
  # If you choose to skip ensemble training (which is slow), use the best
  # fmllr alignments available (tri4a)
  # You can modify this in local/nnet/run_tdnn.sh
  local/nnet3/run_tdnn.sh
  
  exit 0;