#!/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=3
stop_stage=3

# We assume dl_dir (download dir) contains the following
# directories and files. If not, they will be downloaded
# by this script automatically.
#
#  - $dl_dir/icmcasr
#      You can find data_icmcasr, resource_icmcasr inside it.
#      You can download them from https://www.openslr.org/33
#
#  - $dl_dir/musan
#      This directory contains the following directories downloaded from
#       http://www.openslr.org/17/
#
#     - music
#     - noise
#     - speech

# ln -s /your/parent/path/to/ICMC-ASR $PWD/downloa
dl_dir=$PWD/download

. shared/parse_options.sh || exit 1

# vocab size for sentence piece models.
# It will generate data/lang_bbpe_xxx,
# data/lang_bbpe_yyy if the array contains xxx, yyy
vocab_sizes=(
  # 2000
  # 1000
  500
)

# 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 icmcasr manifest"
  # We assume that you have downloaded the icmcasr corpus
  # to $dl_dir/icmcasr
  if [ ! -f data/manifests/.icmcasr_manifests.done ]; then
    mkdir -p data/manifests
    for part in ihm sdm; do
	    lhotse prepare icmcasr --mic ${part} $dl_dir/ICMC-ASR data/manifests
    done
    touch data/manifests/.icmcasr_manifests.done
  fi
fi

if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
  log "Stage 2: Prepare musan manifest"
  # We assume that you have downloaded the musan corpus
  # to data/musan
  if [ ! -f data/manifests/.musan_manifests.done ]; then
    mkdir -p data/manifests
    lhotse prepare musan $dl_dir/musan data/manifests
    touch data/manifests/.musan_manifests.done
  fi
fi

if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
  log "Stage 3: Compute fbank for icmcasr"
  if [ ! -f data/fbank/.icmcasr.done ]; then
    mkdir -p data/fbank
    ./local/compute_fbank_icmcasr.py --perturb-speed True
    touch data/fbank/.icmcasr.done
  fi
fi

if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
  log "Stage 4: Compute fbank for musan"
  if [ ! -f data/fbank/.msuan.done ]; then
    mkdir -p data/fbank
    ./local/compute_fbank_musan.py
    touch data/fbank/.msuan.done
  fi
fi

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

  (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
    cat - $dl_dir/icmcasr/resource_icmcasr/lexicon.txt |
    sort | uniq > $lang_phone_dir/lexicon.txt

  ./local/generate_unique_lexicon.py --lang-dir $lang_phone_dir

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

lang_char_dir=data/lang_char
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Prepare char based lang"
  mkdir -p $lang_char_dir
  # We reuse words.txt from phone based lexicon
  # so that the two can share G.pt later.

  # The transcripts in training set, generated in stage 5
  cp $lang_phone_dir/transcript_words.txt $lang_char_dir/transcript_words.txt

  cat $dl_dir/icmcasr/data_icmcasr/transcript/icmcasr_transcript_v0.8.txt |
  cut -d " " -f 2- > $lang_char_dir/text

  (echo '<eps> 0'; echo '!SIL 1'; echo '<SPOKEN_NOISE> 2'; echo '<UNK> 3';) \
    > $lang_char_dir/words.txt

  cat $lang_char_dir/text | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
     | awk '{print $1" "NR+3}' >> $lang_char_dir/words.txt

  num_lines=$(< $lang_char_dir/words.txt wc -l)
  (echo "#0 $num_lines"; echo "<s> $(($num_lines + 1))"; echo "</s> $(($num_lines + 2))";) \
    >> $lang_char_dir/words.txt

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

  if [ ! -f $lang_char_dir/HLG.fst ]; then
    ./local/prepare_lang_fst.py  --lang-dir $lang_phone_dir --ngram-G ./data/lm/G_3_gram.fst.txt
  fi
fi

if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
  log "Stage 7: Prepare Byte BPE based lang"

  for vocab_size in ${vocab_sizes[@]}; do
    lang_dir=data/lang_bbpe_${vocab_size}
    mkdir -p $lang_dir

    cp $lang_char_dir/words.txt $lang_dir
    cp $lang_char_dir/text $lang_dir

    if [ ! -f $lang_dir/bbpe.model ]; then
      ./local/train_bbpe_model.py \
        --lang-dir $lang_dir \
        --vocab-size $vocab_size \
        --transcript $lang_dir/text
    fi

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