#!/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=15
stage=-1
stop_stage=100

# 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
#
otc_token="<star>"
# ssl or fbank
feature_type="fbank"

dl_dir=$PWD/download
manifests_dir="data/manifests"
feature_dir="data/${feature_type}"
lang_dir="data/lang"
lm_dir="data/lm"

perturb_speed=false

. 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=(
  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: Download LM"
  mkdir -p ${dl_dir}/lm
  if [ ! -e ${dl_dir}/lm/.done ]; then
    ./local/download_lm.py --out-dir=${dl_dir}/lm
    touch ${dl_dir}/lm/.done
  fi
fi

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

  # If you have pre-downloaded it to /path/to/LibriSpeech,
  # you can create a symlink
  #
  #   ln -sfv /path/to/LibriSpeech $dl_dir/LibriSpeech
  #
  if [ ! -d $dl_dir/LibriSpeech/train-clean-100 ]; then
    lhotse download librispeech --full ${dl_dir}
  fi
fi

if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
  log "Stage 1: Prepare LibriSpeech manifest"
  # We assume that you have downloaded the LibriSpeech corpus
  # to $dl_dir/LibriSpeech
  mkdir -p data/manifests
  if [ ! -e data/manifests/.librispeech.done ]; then
    lhotse prepare librispeech -j ${nj} \
      -p dev-clean \
      -p dev-other \
      -p test-clean \
      -p test-other \
      -p train-clean-100 "${dl_dir}/LibriSpeech" "${manifests_dir}"
    touch data/manifests/.librispeech.done
  fi
fi

if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
  log "Stage 2: Compute ${feature_type} feature for librispeech (train-clean-100)"
  mkdir -p "${feature_dir}"  
  if [ ! -e "${feature_dir}/.librispeech.done" ]; then
    if [ "${feature_type}" = ssl ]; then
      ./local/compute_ssl_librispeech.py
    elif [ "${feature_type}" = fbank ]; then
      ./local/compute_fbank_librispeech.py --perturb-speed ${perturb_speed}
    else
      log "Error: not supported --feature-type '${feature_type}'" 
      exit 2
    fi

    touch "${feature_dir}.librispeech.done"
  fi

  if [ ! -e "${feature_dir}/.librispeech-validated.done" ]; then
    log "Validating data/ssl for LibriSpeech"
    parts=(
      train-clean-100
      test-clean
      test-other
      dev-clean
      dev-other
    )
    for part in ${parts[@]}; do
      python3 ./local/validate_manifest.py \
        "${feature_dir}/librispeech_cuts_${part}.jsonl.gz"
    done
    touch "${feature_dir}/.librispeech-validated.done"
  fi
fi

if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
  log "Stage 3: Prepare words.txt"
  mkdir -p ${lang_dir}

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

  local/get_words_from_lexicon.py \
    --lang-dir ${lang_dir} \
    --otc-token ${otc_token}
fi

if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
  log "Stage 4: Prepare BPE based lang"

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

    if [ ! -f "${bpe_lang_dir}/transcript_words.txt" ]; then
      log "Generate data for BPE training"
      files=$(
        find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
        find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
        find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
      )
      for f in ${files[@]}; do
        cat $f | cut -d " " -f 2-
      done > "${bpe_lang_dir}/transcript_words.txt"
    fi

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

    if [ ! -f ${bpe_lang_dir}/L_disambig.pt ]; then
      ./local/prepare_otc_lang_bpe.py \
        --lang-dir "${bpe_lang_dir}" \
        --otc-token "${otc_token}"

      log "Validating ${bpe_lang_dir}/lexicon.txt"
      ./local/validate_bpe_lexicon.py \
        --lexicon ${bpe_lang_dir}/lexicon.txt \
        --bpe-model ${bpe_lang_dir}/bpe.model \
        --otc-token "${otc_token}"
    fi
  done
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}

  if [ ! -f $lang_dir/lexicon.txt ]; then
    (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
      cat - $dl_dir/lm/librispeech-lexicon.txt |
      sort | uniq > $lang_dir/lexicon.txt
  fi

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

if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Prepare G"
  # We assume you have installed kaldilm, if not, please install
  # it using: pip install kaldilm

  mkdir -p "${lm_dir}"
  if [ ! -f ${lm_dir}/G_3_gram.fst.txt ]; then
    # It is used in building HLG
    python3 -m kaldilm \
      --read-symbol-table="${lang_dir}/words.txt" \
      --disambig-symbol='#0' \
      --max-order=3 \
      ${dl_dir}/lm/3-gram.pruned.1e-7.arpa > ${lm_dir}/G_3_gram.fst.txt
  fi

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

if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
  log "Stage 7: Compile HLG"
  # 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}"
    echo "LM DIR: ${lm_dir}"
    ./local/compile_hlg.py \
        --lm-dir "${lm_dir}" \
        --lang-dir "${bpe_lang_dir}"
  done
fi

if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
  log "Stage 7: Compile HLG"
  # 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
  lang_dir="data/lang_phone"
  echo "LM DIR: ${lm_dir}"
  ./local/compile_hlg.py \
      --lm-dir "${lm_dir}" \
      --lang-dir "${lang_dir}"
fi