Yannick Estève / ONTRAC-Kaldi

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egs/sprakbanken/s5/run.sh 6.18 KB
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8dcb6dfcb   Yannick Estève   first commit 
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  #!/bin/bash
  
  . ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
             ## This relates to the queue.
  . ./path.sh # so python3 is on the path if not on the system (we made a link to utils/).a
  
  nj=12
  stage=0
  . utils/parse_options.sh
  
  if [ $stage -le 0 ]; then
    # Download the corpus and prepare parallel lists of sound files and text files
    # Divide the corpus into train, dev and test sets
    local/sprak_data_prep.sh  || exit 1;
  fi
  
  if [ $stage -le 1 ]; then
    # Perform text normalisation, prepare dict folder and LM data transcriptions
    # This setup uses previsously prepared data. eSpeak must be installed and in PATH to use dict_prep.sh
    # local/dict_prep.sh || exit 1;
    local/copy_dict.sh || exit 1;
  fi
  
  if [ $stage -le 2 ]; then
    utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang_tmp data/lang || exit 1;
  fi
  
  if [ $stage -le 3 ]; then
    # Extract mfccs 
    # p was added to the rspecifier (scp,p:$logdir/wav.JOB.scp) in make_mfcc.sh because some 
    # wave files are corrupt 
    # Will return a warning message because of the corrupt audio files, but compute them anyway
    # If this step fails and prints a partial diff, rerun from sprak_data_prep.sh
    for dataset in train test dev; do
      steps/make_mfcc.sh --nj $nj --cmd "$train_cmd" data/$dataset || exit 1;
  
      # Compute cepstral mean and variance normalisation
      steps/compute_cmvn_stats.sh data/$dataset || exit 1;
  
      # Repair data set (remove corrupt data points with corrupt audio)
      utils/fix_data_dir.sh data/$dataset || exit 1;
  
    done
    # Make a subset of the training data with the shortest 120k utterances. 
    utils/subset_data_dir.sh --shortest data/train 120000 data/train_120kshort || exit 1;
  fi
  
  if [ $stage -le 4 ]; then
    # Train LM with irstlm
    local/train_irstlm.sh data/local/transcript_lm/transcripts.uniq 3 "tg" data/lang data/local/train3_lm &> data/local/tg.log || exit 1;
    local/train_irstlm.sh data/local/transcript_lm/transcripts.uniq 4 "fg" data/lang data/local/train4_lm &> data/local/fg.log || exit 1;
  fi
  
  if [ $stage -le 5 ]; then
    # Train monophone model on short utterances
    steps/train_mono.sh --nj $nj --cmd "$train_cmd" \
      data/train_120kshort data/lang exp/mono0a || exit 1;
    utils/mkgraph.sh --mono data/lang_test_tg exp/mono0a exp/mono0a/graph_tg || exit 1;
    steps/decode.sh --nj 12 --cmd "$decode_cmd" \
      exp/mono0a/graph_tg data/dev exp/mono0a/decode_tg_dev || exit 1;
  fi
  
  if [ $stage -le 6 ]; then
    # Train tri1 (delta+delta-delta)
    steps/align_si.sh --nj $nj --cmd "$train_cmd" \
      data/train data/lang exp/mono0a exp/mono0a_ali || exit 1;
    steps/train_deltas.sh --cmd "$train_cmd" \
      3000 40000 data/train data/lang exp/mono0a_ali exp/tri1 || exit 1;
  
    # Decode dev set with both LMs
    utils/mkgraph.sh data/lang_test_tg exp/tri1 exp/tri1/graph_tg || exit 1;
    utils/mkgraph.sh data/lang_test_fg exp/tri1 exp/tri1/graph_fg || exit 1; 
    steps/decode.sh --nj 12 --cmd "$decode_cmd" \
      exp/tri1/graph_fg data/dev exp/tri1/decode_fg_dev || exit 1;
    steps/decode.sh --nj 12 --cmd "$decode_cmd" \
      exp/tri1/graph_tg data/dev exp/tri1/decode_tg_dev || exit 1;
  fi
  
  if [ $stage -le 7 ]; then
    # Train tri2a (delta + delta-delta)
    steps/align_si.sh --nj $nj --cmd "$train_cmd" \
      data/train data/lang exp/tri1 exp/tri1_ali || exit 1;
    steps/train_deltas.sh --cmd "$train_cmd" \
      5000 60000 data/train data/lang exp/tri1_ali exp/tri2a || exit 1;
    utils/mkgraph.sh data/lang_test_tg exp/tri2a exp/tri2a/graph_tg || exit 1;
    steps/decode.sh --nj 12 --cmd "$decode_cmd" \
      exp/tri2a/graph_tg data/dev exp/tri2a/decode_tg_dev || exit 1;
  fi
  
  if [ $stage -le 8 ]; then
    # Train tri2b (LDA+MLLT)
    steps/align_si.sh --nj $nj --cmd "$train_cmd" \
      data/train data/lang exp/tri2a exp/tri2a_ali || exit 1;
    steps/train_lda_mllt.sh --cmd "$train_cmd" \
      --splice-opts "--left-context=5 --right-context=5" \
      6500 75000 data/train data/lang exp/tri2a_ali exp/tri2b || exit 1;
    utils/mkgraph.sh data/lang_test_tg exp/tri2b exp/tri2b/graph_tg || exit 1;
    steps/decode.sh --nj 12 --cmd "$decode_cmd" \
      exp/tri2b/graph_tg data/dev exp/tri2b/decode_tg_dev || exit 1;
  fi
  
  if [ $stage -le 9 ]; then
    # From 2b system, train 3b which is LDA + MLLT + SAT.
    steps/align_si.sh  --nj $nj --cmd "$train_cmd" \
      --use-graphs true data/train data/lang exp/tri2b exp/tri2b_ali  || exit 1;
    steps/train_sat.sh --cmd "$train_cmd" \
      7500 100000 data/train data/lang exp/tri2b_ali exp/tri3b || exit 1;
  
    # Decode dev with 4gram and 3gram LMs
    utils/mkgraph.sh data/lang_test_tg exp/tri3b exp/tri3b/graph_tg || exit 1;
    steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 12 \
      exp/tri3b/graph_tg data/dev exp/tri3b/decode_tg_dev || exit 1;
    utils/mkgraph.sh data/lang_test_fg exp/tri3b exp/tri3b/graph_fg || exit 1;
    steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 12 \
      exp/tri3b/graph_fg data/dev exp/tri3b/decode_fg_dev || exit 1;
  
    # Decode test with 4gram and 3gram LMs
    # there are fewer speaker (n=7) and decoding usually ends up waiting
    # for a single job so we use --num-threads 2 to speed up
    steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 7 --num-threads 2 \
      exp/tri3b/graph_tg data/test exp/tri3b/decode_tg_test || exit 1;
    steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 7 --num-threads 2 \
      exp/tri3b/graph_fg data/test exp/tri3b/decode_fg_test || exit 1;
  fi
  
  if [ $stage -le 10 ]; then
    # Alignment used to train nnets and sgmms
    steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \
      data/train data/lang exp/tri3b exp/tri3b_ali || exit 1;
  fi
  
  
  ## Works
  #local/sprak_run_nnet_cpu.sh tg dev 
  
  ## Works
  #local/sprak_run_sgmm2.sh dev
  
  
  # Run neural network setups based in the TEDLIUM recipe
  
  # Running the nnet3-tdnn setup will train an ivector extractor that
  # is used by the subsequent nnet3 and chain systems (why --stage is
  # specified)
  #local/nnet3/run_tdnn.sh --tdnn-affix "0" --nnet3-affix ""
  
  # nnet3 LSTM
  #local/nnet3/run_lstm.sh --stage 13 --affix "0"
  
  # nnet3 bLSTM
  #local/nnet3/run_blstm.sh --stage 12
  
  
  
  # chain TDNN
  # This setup creates a new lang directory that is also used by the
  # TDNN-LSTM system
  #local/chain/run_tdnn.sh --stage 14
  
  # chain TDNN-LSTM
  local/chain/run_tdnn_lstm.sh --stage 17
  
  
  # Getting results [see RESULTS file]
  local/generate_results_file.sh 2> /dev/null > RESULTS