#!/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

# Run step 0 to step 8 by default
stage=0
stop_stage=8

# Compute fbank features for a subset of splits from `start` (inclusive) to `stop` (exclusive)
start=0
stop=-1  # -1 means until the end

# If true, skip storing GigaSpeech fbank features; only produce the (trimmed)
# cut manifests for on-the-fly feature extraction during training.
on_the_fly=true

# If false (default), skip all musan steps (download, manifest, fbank).
use_musan=false

# Note: This script just prepares the minimal requirements needed by a
# transducer training with bpe units.
#
# If you want to use ngram, please continue running prepare_lm.sh after
# you succeed in running this script.
#
# This script also contains the steps to generate phone based units, but they
# will not run automatically, you can generate the phone based units by
# bash prepare.sh --stage 9 --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/GigaSpeech
#      You can find audio, dict, GigaSpeech.json inside it.
#      You can apply for the download credentials by following
#      https://github.com/SpeechColab/GigaSpeech#download
#
#  - $dl_dir/lm
#      This directory contains the language model downloaded from
#        https://huggingface.co/wgb14/gigaspeech_lm
#
#        - 3gram_pruned_1e7.arpa.gz
#        - 4gram.arpa.gz
#        - lexicon.txt
#
#  - $dl_dir/musan
#      This directory contains the following directories downloaded from
#       http://www.openslr.org/17/
#
#       - music
#       - noise
#       - speech

dl_dir=$PWD/download

. 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
  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 "Running prepare.sh"

log "dl_dir: $dl_dir"

if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
  log "Stage -1: Download LM"
  # We assume that you have installed the git-lfs, if not, you could install it
  # using: `sudo apt-get install git-lfs && git-lfs install`
  [ ! -e $dl_dir/lm ] && mkdir -p $dl_dir/lm
  git clone https://huggingface.co/wgb14/gigaspeech_lm $dl_dir/lm
  gunzip -c $dl_dir/lm/3gram_pruned_1e7.arpa.gz > $dl_dir/lm/3gram_pruned_1e7.arpa
  gunzip -c $dl_dir/lm/4gram.arpa.gz > $dl_dir/lm/4gram.arpa
fi

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

  [ ! -e $dl_dir/GigaSpeech ] && mkdir -p $dl_dir/GigaSpeech

  # If you have pre-downloaded it to /path/to/GigaSpeech,
  # you can create a symlink
  #
  #   ln -svf /path/to/GigaSpeech $dl_dir/GigaSpeech
  #
  if [ ! -d $dl_dir/GigaSpeech/audio ] && [ ! -f $dl_dir/GigaSpeech.json ]; then
    # Check credentials.
    if [ ! -f $dl_dir/password ]; then
      echo -n "$0: Please apply for the download credentials by following"
      echo -n "https://github.com/SpeechColab/GigaSpeech#download"
      echo " and save it to $dl_dir/password."
      exit 1;
    fi

    PASSWORD=`cat $dl_dir/password 2>/dev/null`
    if [ -z "$PASSWORD" ]; then
      echo "$0: Error, $dl_dir/password is empty."
      exit 1;
    fi

    PASSWORD_MD5=`echo $PASSWORD | md5sum | cut -d ' ' -f 1`
    if [[ $PASSWORD_MD5 != "dfbf0cde1a3ce23749d8d81e492741b8" ]]; then
      echo "$0: Error, invalid $dl_dir/password."
      exit 1;
    fi

    # Download XL, DEV and TEST sets by default.
    # Support hosts:
    # 1. oss
    # 2. tsinghua
    # 3. speechocean
    # 4. magicdata
    lhotse download gigaspeech \
      --host magicdata \
      --subset DEV \
      --subset TEST \
      --subset XL \
      $dl_dir/password $dl_dir/GigaSpeech
  fi

  # If you have pre-downloaded it to /path/to/musan,
  # you can create a symlink
  #
  #   ln -svf /path/to/musan $dl_dir/
  #
  if [ $use_musan == true ] && [ ! -d $dl_dir/musan ]; then
    lhotse download musan $dl_dir
  fi
fi

if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
  log "Stage 1: Prepare GigaSpeech manifest (may take 15 minutes)"
  # We assume that you have downloaded the GigaSpeech corpus
  # to $dl_dir/GigaSpeech
  mkdir -p data/manifests
  lhotse prepare gigaspeech \
    --subset XL \
    --subset DEV \
    --subset TEST \
    -j $nj \
    $dl_dir/GigaSpeech data/manifests
fi

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

if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
  log "Stage 3: Preprocess GigaSpeech manifest"
  if [ ! -f data/fbank/.preprocess_complete ]; then
    python3 ./local/preprocess_gigaspeech.py
    touch data/fbank/.preprocess_complete
  fi
fi

if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
  log "Stage 4: Compute features for DEV and TEST subsets of GigaSpeech."
  python3 ./local/compute_fbank_gigaspeech.py --on-the-fly $on_the_fly
fi

if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
  log "Stage 5: Split XL subset into pieces (may take 5 minutes)"
  num_per_split=50
  split_dir=data/fbank/gigaspeech_XL_split
  if [ ! -f $split_dir/.split_completed ]; then
    lhotse split-lazy ./data/fbank/gigaspeech_cuts_XL_raw.jsonl.gz $split_dir $num_per_split
    touch $split_dir/.split_completed
  fi
fi

if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Compute features for XL"
  split_dir=data/fbank/gigaspeech_XL_split
  num_splits=$(find $split_dir -name "gigaspeech_cuts_XL_raw.*.jsonl.gz" | wc -l)
  python3 ./local/compute_fbank_gigaspeech_splits.py \
    --num-workers 20 \
    --batch-duration 600 \
    --num-splits $num_splits \
    --start $start \
    --stop $stop \
    --on-the-fly $on_the_fly
fi

if [ $use_musan == true ] && [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
    log "Stage 7: Compute fbank for musan"
    mkdir -p data/fbank
    ./local/compute_fbank_musan.py
fi

if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
  log "Stage 8: Prepare BPE based lang"
  for vocab_size in ${vocab_sizes[@]}; do
    lang_dir=data/lang_bpe_${vocab_size}
    mkdir -p $lang_dir

    if [ ! -f $lang_dir/transcript_words.txt ]; then
      log "Generate data for BPE training"
      gunzip -c "data/manifests/gigaspeech_supervisions_XL.jsonl.gz" \
        | jq '.text' \
        | sed 's/"//g' \
        > $lang_dir/transcript_words.txt

      # Delete utterances with garbage meta tags
      garbage_utterance_tags="<SIL> <MUSIC> <NOISE> <OTHER>"
      for tag in $garbage_utterance_tags; do
        sed -i "/${tag}/d" $lang_dir/transcript_words.txt
      done

      # Delete punctuations in utterances
      punctuation_tags="<COMMA> <EXCLAMATIONPOINT> <PERIOD> <QUESTIONMARK>"
      for tag in $punctuation_tags; do
        sed -i "s/${tag}//g" $lang_dir/transcript_words.txt
      done

      # Ensure space only appears once
      sed -i 's/\t/ /g' $lang_dir/transcript_words.txt
      sed -i 's/[ ][ ]*/ /g' $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
  done
fi

if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
  log "Stage 9: 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/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
fi