#!/usr/bin/env bash
# Copyright 2024 Johns Hopkins University  (Amir Hussein)
# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

set -eou pipefail

nj=20
stage=-1
stop_stage=6

# We assume dl_dir (download dir) contains the following
# directories and files.
#
#  - $dl_dir/mgb2
#
#      You can download the data from
#
#
#  - $dl_dir/musan
#      This directory contains the following directories downloaded from
#       http://www.openslr.org/17/
#
#     - music
#     - noise
#     - speech
#

dl_dir=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=(
  4000
)

# 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 0: Download data"
  
  # Note: Download SEAME from https://catalog.ldc.upenn.edu/LDC2015S04
  #
  # downlaod the splits https://github.com/zengzp0912/SEAME-dev-set.git to $dl_dir
  # 
  # If you have pre-downloaded it to /path/to/seame,
  # you can create a symlink
  #
  #   ln -sfv /path/to/seame $dl_dir/seame

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

fbank=data_seame/fbank
manifests=data_seame/manifests
mkdir -p $manifests

if [ $stage -le 0] && [ $stop_stage -ge 0 ]; then
  log "Stage 0: Prepare seame manifest"
  # We assume that you have downloaded the corpus
  # to $dl_dir/seame
    lhotse prepare seame $dl_dir/seame $dl_dir/SEAME-dev-set data/manifests
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
  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: Compute fbank features"
  mkdir -p ${fbank}
  python local/compute_fbank_gpu_seame.py
  gunzip -c $fbank/cuts_train.jsonl.gz | shuf | gzip -c > ${fbank}/cuts_train_shuf.jsonl.gz
fi

if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Prepare BPE based lang"
  for vocab_size in ${vocab_sizes[@]}; do
    lang_dir=data_seame/lang_bpe_${vocab_size}
    mkdir -p ${lang_dir}
    if [ ! -f $lang_dir/transcript_words.txt ]; then
      log "Generate text for BPE training from data_seame/fbank/cuts_train_shuf.jsonl.gz"
      python local/prepare_transcripts.py --cut ${fbank}/cuts_train_shuf.jsonl.gz --langdir ${lang_dir} 
    fi
    source data_seame/manifests/token.man.1
    ./local/train_bpe_model.py \
      --lang-dir $lang_dir \
      --vocab-size $vocab_size \
      --transcript $lang_dir/transcript_words.txt \
      --predef-symbols "$bpe_nlsyms"
  done
fi