#!/usr/bin/env bash

set -eou pipefail

nj=16
stage=-1
stop_stage=100

# Split data/${lang}set to this number of pieces
# This is to avoid OOM during feature extraction.
num_splits=1000

# We assume dl_dir (download dir) contains the following
# directories and files. If not, they will be downloaded
# by this script automatically.
#
#  - $dl_dir/$release/$lang
#      This directory contains the following files downloaded from
#       https://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/${release}/${release}-${lang}.tar.gz
#
#     - clips
#     - dev.tsv
#     - invalidated.tsv
#     - other.tsv
#     - reported.tsv
#     - test.tsv
#     - train.tsv
#     - validated.tsv

dl_dir=$PWD/download
release=cv-corpus-13.0-2023-03-09
lang=en

. shared/parse_options.sh || exit 1

# All files generated by this script are saved in "data/${lang}".
# You can safely remove "data/${lang}" and rerun this script to regenerate it.
mkdir -p data/${lang}

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

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

if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
  log "Stage 1: Prepare CommonVoice manifest"
  # We assume that you have downloaded the CommonVoice corpus
  # to $dl_dir/$release
  mkdir -p data/${lang}/manifests
  if [ ! -e data/${lang}/manifests/.cv-${lang}.done ]; then
    lhotse prepare commonvoice --language $lang -j $nj $dl_dir/$release data/${lang}/manifests
    touch data/${lang}/manifests/.cv-${lang}.done
  fi
fi

if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
  log "Stage 2: Preprocess CommonVoice manifest"
  if [ ! -e data/${lang}/fbank/.preprocess_complete ]; then
    ./local/preprocess_commonvoice.py  --language $lang
    touch data/${lang}/fbank/.preprocess_complete
  fi
fi

if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
  log "Stage 3: Compute fbank for dev and test subsets of CommonVoice"
  mkdir -p data/${lang}/fbank
  if [ ! -e data/${lang}/fbank/.cv-${lang}_dev_test.done ]; then
    ./local/compute_fbank_commonvoice_dev_test.py --language $lang
    touch data/${lang}/fbank/.cv-${lang}_dev_test.done
  fi
fi

if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
  log "Stage 4: Split train subset into ${num_splits} pieces"
  split_dir=data/${lang}/fbank/cv-${lang}_train_split_${num_splits}
  if [ ! -e $split_dir/.cv-${lang}_train_split.done ]; then
    lhotse split $num_splits ./data/${lang}/fbank/cv-${lang}_cuts_train_raw.jsonl.gz $split_dir
    touch $split_dir/.cv-${lang}_train_split.done
  fi
fi

if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
  log "Stage 5: Compute features for train subset of CommonVoice"
  if [ ! -e data/${lang}/fbank/.cv-${lang}_train.done ]; then
    ./local/compute_fbank_commonvoice_splits.py \
      --num-workers $nj \
      --batch-duration 600 \
      --start 0 \
      --num-splits $num_splits \
      --language $lang
    touch data/${lang}/fbank/.cv-${lang}_train.done
  fi
fi

if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Combine features for train"
  if [ ! -f data/${lang}/fbank/cv-${lang}_cuts_train.jsonl.gz ]; then
    pieces=$(find data/${lang}/fbank/cv-${lang}_train_split_${num_splits} -name "cv-${lang}_cuts_train.*.jsonl.gz")
    lhotse combine $pieces data/${lang}/fbank/cv-${lang}_cuts_train.jsonl.gz
  fi
fi