#!/usr/bin/env bash

# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

set -eou pipefail

nj=60
stage=-1
stop_stage=9

# We assume dl_dir (download dir) contains the following
# directories and files. If not, they will be downloaded
# by this script automatically.
#
#  - $dl_dir/LibriSpeech
#      You can find BOOKS.TXT, test-clean, train-clean-360, etc, inside it.
#      You can download them from https://www.openslr.org/12
#
#  - $dl_dir/lm
#      This directory contains the following files downloaded from
#       http://www.openslr.org/resources/11
#
#        - 3-gram.pruned.1e-7.arpa.gz
#        - 3-gram.pruned.1e-7.arpa
#        - 4-gram.arpa.gz
#        - 4-gram.arpa
#        - librispeech-vocab.txt
#        - librispeech-lexicon.txt
#        - librispeech-lm-norm.txt.gz
#
#  - $dl_dir/musan
#      This directory contains the following directories downloaded from
#       http://www.openslr.org/17/
#
#     - music
#     - noise
#     - speech
dl_dir=$PWD/download
espnet_path=/home/wtc7/espnet/egs2/MUCS/asr1/data/hi-en/

. shared/parse_options.sh || exit 1

# vocab size for sentence piece models.
# It will generate data/lang_bpe_xxx,
# data/lang_bpe_yyy if the array contains xxx, yyy
vocab_sizes=(
  # 5000
  # 2000
  # 1000
  200
)

# All files generated by this script are saved in "data".
# You can safely remove "data" and rerun this script to regenerate it.
mkdir -p data

log() {
  # This function is from espnet
  local fname=${BASH_SOURCE[1]##*/}
  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}

log "dl_dir: $dl_dir"

if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
  log "Stage -1: prepare LM files"
  mkdir -p $dl_dir/lm
  if [ ! -e $dl_dir/lm/.done ]; then
    ./local/prepare_lm_files.py --out-dir=$dl_dir/lm --data-path=$espnet_path --mode="train"
    # touch $dl_dir/lm/.done
  fi
fi

if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
  log "Stage 0: Download data"
fi

if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
  log "Stage 1: Prepare MUCS manifest"
  # We assume that you have downloaded the LibriSpeech corpus
  # to $dl_dir/LibriSpeech
  mkdir -p data/manifests
  if [ ! -e data/manifests/.mucs.done ]; then
    # lhotse prepare mucs -j $nj $dl_dir/hi-en data/manifests
    ./local/prepare_manifest.py "$espnet_path" $nj data/manifests

    touch data/manifests/.mucs.done
  fi
fi

if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
  log "Stage 3: Compute fbank for mucs"
  mkdir -p data/fbank
  if [ ! -e data/fbank/.mucs.done ]; then
    ./local/compute_fbank_mucs.py
    # touch data/fbank/.mucs.done
  fi

  # exit

  if [ ! -e data/fbank/.mucs-validated.done ]; then
    log "Validating data/fbank for mucs"
    parts=(
      train
      test
      dev
    )
    for part in ${parts[@]}; do
      python3 ./local/validate_manifest.py \
        data/fbank/mucs_cuts_${part}.jsonl.gz
    done
    # touch data/fbank/.mucs-validated.done
  fi
fi



if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
  log "Stage 5: Prepare phone based lang"
  lang_dir=data/lang_phone
  mkdir -p $lang_dir

  (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
    cat - $dl_dir/lm/mucs_lexicon.txt |
    sort | uniq > $lang_dir/lexicon.txt

  if [ ! -f $lang_dir/L_disambig.pt ]; then
    ./local/prepare_lang.py --lang-dir $lang_dir
  fi

  if [ ! -f $lang_dir/L.fst ]; then
    log "Converting L.pt to L.fst"
    ./shared/convert-k2-to-openfst.py \
      --olabels aux_labels \
      $lang_dir/L.pt \
      $lang_dir/L.fst
  fi

  if [ ! -f $lang_dir/L_disambig.fst ]; then
    log "Converting L_disambig.pt to L_disambig.fst"
    ./shared/convert-k2-to-openfst.py \
      --olabels aux_labels \
      $lang_dir/L_disambig.pt \
      $lang_dir/disambig_L.fst
  fi
fi


if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Prepare BPE based lang"

  for vocab_size in ${vocab_sizes[@]}; do
    lang_dir=data/lang_bpe_${vocab_size}
    mkdir -p $lang_dir
    # We reuse words.txt from phone based lexicon
    # so that the two can share G.pt later.
    cp data/lang_phone/words.txt $lang_dir

    if [ ! -f $lang_dir/transcript_words.txt ]; then
      log "Generate data for BPE training"
      cp download/lm/mucs_vocab_text.txt $lang_dir/transcript_words.txt
    fi

    if [ ! -f $lang_dir/bpe.model ]; then
      ./local/train_bpe_model.py \
        --lang-dir $lang_dir \
        --vocab-size $vocab_size \
        --transcript $lang_dir/transcript_words.txt
    fi

    if [ ! -f $lang_dir/L_disambig.pt ]; then
      ./local/prepare_lang_bpe.py --lang-dir $lang_dir

      log "Validating $lang_dir/lexicon.txt"
      ./local/validate_bpe_lexicon.py \
        --lexicon $lang_dir/lexicon.txt \
        --bpe-model $lang_dir/bpe.model
    fi

    if [ ! -f $lang_dir/L.fst ]; then
      log "Converting L.pt to L.fst"
      ./shared/convert-k2-to-openfst.py \
        --olabels aux_labels \
        $lang_dir/L.pt \
        $lang_dir/L.fst
    fi

    if [ ! -f $lang_dir/L_disambig.fst ]; then
      log "Converting L_disambig.pt to L_disambig.fst"
      ./shared/convert-k2-to-openfst.py \
        --olabels aux_labels \
        $lang_dir/L_disambig.pt \
        $lang_dir/L_disambig.fst
    fi
  done
fi

if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
  log "Stage 7: Prepare bigram token-level P for MMI training"

  for vocab_size in ${vocab_sizes[@]}; do
    lang_dir=data/lang_bpe_${vocab_size}

    # if [ ! -f $lang_dir/transcript_tokens.txt ]; then
    #   ./local/convert_transcript_words_to_tokens.py \
    #     --lexicon $lang_dir/lexicon.txt \
    #     --transcript $lang_dir/transcript_words.txt \
    #     --oov "<UNK>" \
    #     > $lang_dir/transcript_tokens.txt
    # fi

    if [ ! -f $lang_dir/lm_3.arpa ]; then
      ./shared/make_kn_lm.py \
        -ngram-order 3 \
        -text $lang_dir/transcript_words.txt \
        -lm $lang_dir/lm_3.arpa
    fi

    if [ ! -f $lang_dir/lm_4.arpa ]; then
      ./shared/make_kn_lm.py \
        -ngram-order 4 \
        -text $lang_dir/transcript_words.txt \
        -lm $lang_dir/lm_4.arpa
    fi

    # if [ ! -f $lang_dir/P.fst.txt ]; then
    #   python3 -m kaldilm \
    #     --read-symbol-table="$lang_dir/tokens.txt" \
    #     --disambig-symbol='#0' \
    #     --max-order=2 \
    #     $lang_dir/P.arpa > $lang_dir/P.fst.txt
    # fi
  done
fi

if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
  log "Stage 8: Prepare G"
  # We assume you have install kaldilm, if not, please install
  # it using: pip install kaldilm

  mkdir -p data/lm
  if [ ! -f data/lm/G_3_gram.fst.txt ]; then
    # It is used in building HLG
    python3 -m kaldilm \
      --read-symbol-table="data/lang_phone/words.txt" \
      --disambig-symbol='#0' \
      --max-order=3 \
      data/lang_bpe_200/lm_3.arpa > data/lm/G_3_gram.fst.txt
  fi

  if [ ! -f data/lm/G_4_gram.fst.txt ]; then
    # It is used in building HLG
    python3 -m kaldilm \
      --read-symbol-table="data/lang_phone/words.txt" \
      --disambig-symbol='#0' \
      --max-order=3 \
      data/lang_bpe_200/lm_4.arpa > data/lm/G_4_gram.fst.txt
  fi

  # if [ ! -f data/lm/G_4_gram.fst.txt ]; then
  #   # It is used for LM rescoring
  #   python3 -m kaldilm \
  #     --read-symbol-table="data/lang_phone/words.txt" \
  #     --disambig-symbol='#0' \
  #     --max-order=4 \
  #     $dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
  # fi
fi

if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
  log "Stage 9: Compile HLG"
  # ./local/compile_hlg.py --lang-dir data/lang_phone

  # Note If ./local/compile_hlg.py throws OOM,
  # please switch to the following command
  #
  # ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone

  for vocab_size in ${vocab_sizes[@]}; do
    lang_dir=data/lang_bpe_${vocab_size}
    ./local/compile_hlg.py --lang-dir $lang_dir

    # Note If ./local/compile_hlg.py throws OOM,
    # please switch to the following command
    #
    # ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
  done
fi

# Compile LG for RNN-T fast_beam_search decoding
if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
  log "Stage 10: Compile LG"
  ./local/compile_lg.py --lang-dir data/lang_phone

  for vocab_size in ${vocab_sizes[@]}; do
    lang_dir=data/lang_bpe_${vocab_size}
    ./local/compile_lg.py --lang-dir $lang_dir
  done
fi

if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
  log "Stage 11: Generate LM training data"

  for vocab_size in ${vocab_sizes[@]}; do
    log "Processing vocab_size == ${vocab_size}"
    lang_dir=data/lang_bpe_${vocab_size}
    out_dir=data/lm_training_bpe_${vocab_size}
    mkdir -p $out_dir

    ./local/prepare_lm_training_data.py \
      --bpe-model $lang_dir/bpe.model \
      --lm-data $dl_dir/lm/librispeech-lm-norm.txt \
      --lm-archive $out_dir/lm_data.pt
  done
fi

if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
  log "Stage 12: Generate LM validation data"

  for vocab_size in ${vocab_sizes[@]}; do
    log "Processing vocab_size == ${vocab_size}"
    out_dir=data/lm_training_bpe_${vocab_size}
    mkdir -p $out_dir

    if [ ! -f $out_dir/valid.txt ]; then
      files=$(
        find "$dl_dir/LibriSpeech/dev-clean" -name "*.trans.txt"
        find "$dl_dir/LibriSpeech/dev-other" -name "*.trans.txt"
      )
      for f in ${files[@]}; do
        cat $f | cut -d " " -f 2-
      done > $out_dir/valid.txt
    fi

    lang_dir=data/lang_bpe_${vocab_size}
    ./local/prepare_lm_training_data.py \
      --bpe-model $lang_dir/bpe.model \
      --lm-data $out_dir/valid.txt \
      --lm-archive $out_dir/lm_data-valid.pt
  done
fi

if [ $stage -le 13 ] && [ $stop_stage -ge 13 ]; then
  log "Stage 13: Generate LM test data"

  for vocab_size in ${vocab_sizes[@]}; do
    log "Processing vocab_size == ${vocab_size}"
    out_dir=data/lm_training_bpe_${vocab_size}
    mkdir -p $out_dir

    if [ ! -f $out_dir/test.txt ]; then
      files=$(
        find "$dl_dir/LibriSpeech/test-clean" -name "*.trans.txt"
        find "$dl_dir/LibriSpeech/test-other" -name "*.trans.txt"
      )
      for f in ${files[@]}; do
        cat $f | cut -d " " -f 2-
      done > $out_dir/test.txt
    fi

    lang_dir=data/lang_bpe_${vocab_size}
    ./local/prepare_lm_training_data.py \
      --bpe-model $lang_dir/bpe.model \
      --lm-data $out_dir/test.txt \
      --lm-archive $out_dir/lm_data-test.pt
  done
fi

if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then
  log "Stage 14: Sort LM training data"
  # Sort LM training data by sentence length in descending order
  # for ease of training.
  #
  # Sentence length equals to the number of BPE tokens
  # in a sentence.

  for vocab_size in ${vocab_sizes[@]}; do
    out_dir=data/lm_training_bpe_${vocab_size}
    mkdir -p $out_dir
    ./local/sort_lm_training_data.py \
      --in-lm-data $out_dir/lm_data.pt \
      --out-lm-data $out_dir/sorted_lm_data.pt \
      --out-statistics $out_dir/statistics.txt

    ./local/sort_lm_training_data.py \
      --in-lm-data $out_dir/lm_data-valid.pt \
      --out-lm-data $out_dir/sorted_lm_data-valid.pt \
      --out-statistics $out_dir/statistics-valid.txt

    ./local/sort_lm_training_data.py \
      --in-lm-data $out_dir/lm_data-test.pt \
      --out-lm-data $out_dir/sorted_lm_data-test.pt \
      --out-statistics $out_dir/statistics-test.txt
  done
fi