#!/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=4
stop_stage=4

# 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 mdm; 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: Apply GSS enhancement on MDM data (this stage requires a GPU)"
  # We assume that you have installed the GSS package: https://github.com/desh2608/gss
  local/prepare_icmc_gss.sh --stage 1 --stop_stage 6 data/manifests exp/icmc_gss
fi

if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
  log "Stage 4: Compute fbank for icmcasr"
  if [ ! -f data/fbank/.icmcasr.done ]; then
    mkdir -p data/fbank
    ./local/compute_fbank_icmcasr.py --perturb-speed True
    echo "Combining manifests"
   lhotse combine data/manifests/cuts_train_{ihm,ihm_rvb,sdm,gss}.jsonl.gz - | shuf |\
     gzip -c > data/manifests/cuts_train_all.jsonl.gz
    touch data/fbank/.icmcasr.done
  fi
fi

if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
  log "Stage 5: 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 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Prepare G.fst"
  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