#!/usr/bin/env bash

# Copyright 2019 Nagoya University (MAsao Someki)
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

. ./path.sh || exit 1;
. ./cmd.sh || exit 1;

# general configuration
backend=pytorch
stage=-1       # start from -1 if you need to start from data download
stop_stage=100
ngpu=1         # number of gpus ("0" uses cpu, otherwise use gpu)
debugmode=1
dumpdir=dump   # directory to dump full features
N=0            # number of minibatches to be used (mainly for debugging). "0" uses all minibatches.
verbose=0      # verbose option
resume=        # Resume the training from snapshot
nj=10
sampling_rate=48000

# feature configuration
do_delta=false

preprocess_config=conf/specaug.yaml
lm_config=conf/lm.yaml
train_config=conf/train.yaml
decode_config=conf/decode.yaml

lm_resume=
lmtag=
# decoding parameter
n_average=10 # use 1 for RNN models
recog_model=model.acc.best # set a model to be used for decoding: 'model.acc.best' or 'model.loss.best'

# root directory of db
db_root=./downloads

# exp tag
tag="" # tag for managing experiments.

. utils/parse_options.sh || exit 1;

# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail

train_set=tr_no_dev_sp
train_dev=dev
recog_set="dev eval1"

if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
    echo "stage -1: Data Download"
    local/download.sh ${db_root}
fi

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    ### Task dependent. You have to make data the following preparation part by yourself.
    ### But you can utilize Kaldi recipes in most cases
    echo "stage 0: Data Preparation"
    # Initial normalization of the data
    local/data_prep.sh ${db_root}/jsut_ver1.1 data/train $sampling_rate
    utils/validate_data_dir.sh --no-feats data/train
    # changing the sampling rate option in pitch.conf and fbank.conf
    local/change_sampling_rate.sh $sampling_rate
fi

feat_tr_dir=${dumpdir}/${train_set}; mkdir -p ${feat_tr_dir}
feat_dt_dir=${dumpdir}/${train_dev}; mkdir -p ${feat_dt_dir}
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    ### Task dependent. You have to design training and dev sets by yourself.
    ### But you can utilize Kaldi recipes in most cases
    echo "stage 1: Feature Generation"
    fbankdir=fbank
    # Generate the fbank features; by default 80-dimensional fbanks with pitch on each frame
    steps/make_fbank_pitch.sh --cmd "$train_cmd" \
                              --nj ${nj} \
		              --write_utt2num_frames true \
                              data/train \
                              exp/make_fbank/train  \
                              ${fbankdir}
    # make a dev set
    utils/subset_data_dir.sh --first data/train   500 data/deveval
    utils/subset_data_dir.sh --first data/deveval 250 data/eval1
    utils/subset_data_dir.sh --last  data/deveval 250 data/dev
    n=$(( $(wc -l < data/train/wav.scp) - 500 ))
    utils/subset_data_dir.sh --last data/train ${n} data/tr_no_dev

    # speed-perturbed
    utils/perturb_data_dir_speed.sh 0.9 data/tr_no_dev data/temp1
    utils/perturb_data_dir_speed.sh 1.0 data/tr_no_dev data/temp2
    utils/perturb_data_dir_speed.sh 1.1 data/tr_no_dev data/temp3
    utils/combine_data.sh --extra-files utt2uniq data/${train_set} data/temp1 data/temp2 data/temp3
    rm -r data/temp1 data/temp2 data/temp3
    steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj 32 --write_utt2num_frames true \
        data/${train_set} exp/make_fbank/${train_set} ${fbankdir}
    utils/fix_data_dir.sh data/${train_set}

    # compute global CMVN
    compute-cmvn-stats scp:data/${train_set}/feats.scp data/${train_set}/cmvn.ark

    # dump features for training
    dump.sh --cmd "$train_cmd" --nj ${nj} --do_delta ${do_delta} \
        data/${train_set}/feats.scp data/${train_set}/cmvn.ark exp/dump_feats/train ${feat_tr_dir}
    dump.sh --cmd "$train_cmd" --nj 4 --do_delta ${do_delta} \
        data/${train_dev}/feats.scp data/${train_set}/cmvn.ark exp/dump_feats/dev ${feat_dt_dir}
    for rtask in ${recog_set}; do
    	feat_recog_dir=${dumpdir}/${rtask}/delta${do_delta}; mkdir -p ${feat_recog_dir}
    	dump.sh --cmd "$train_cmd" --nj 4 --do_delta ${do_delta} \
        	data/${rtask}/feats.scp data/${train_set}/cmvn.ark exp/dump_feats/recog/${rtask} \
        	${feat_recog_dir}
    done
fi

dict=data/lang_1char/tr_no_dev_units.txt
echo "dictionary: ${dict}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    ### Task dependent. You have to check non-linguistic symbols used in the corpus.
    echo "stage 2: Dictionary and Json Data Preparation"
    mkdir -p data/lang_1char/
    echo "<unk> 1" > ${dict} # <unk> must be 1, 0 will be used for "blank" in CTC
    text2token.py -s 1 -n 1 data/tr_no_dev/text | cut -f 2- -d" " | tr " " "\n" \
    | sort | uniq | grep -v -e '^\s*$' | awk '{print $0 " " NR+1}' >> ${dict}
    wc -l ${dict}
    # make json labels
    data2json.sh --feat ${feat_tr_dir}/feats.scp \
         data/${train_set} ${dict} > ${feat_tr_dir}/data.json
    data2json.sh --feat ${feat_dt_dir}/feats.scp \
         data/${train_dev} ${dict} > ${feat_dt_dir}/data.json
    for rtask in ${recog_set}; do
     	feat_recog_dir=${dumpdir}/${rtask}/delta${do_delta}
     	data2json.sh --feat ${feat_recog_dir}/feats.scp \
         	data/${rtask} ${dict} > ${feat_recog_dir}/data.json
    done
fi


# You can skip this and remove --rnnlm option in the recognition (stage 5)
if [ -z ${lmtag} ]; then
    lmtag=$(basename ${lm_config%.*})
fi
lmexpname=train_rnnlm_${backend}_${lmtag}
lmexpdir=exp/${lmexpname}
mkdir -p ${lmexpdir}

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    echo "stage 3: LM Preparation"
    lmdatadir=data/local/lm_train
    mkdir -p ${lmdatadir}
    text2token.py -s 1 -n 1 data/tr_no_dev/text | cut -f 2- -d" " \
        > ${lmdatadir}/train.txt
    text2token.py -s 1 -n 1 data/${train_dev}/text | cut -f 2- -d" " \
        > ${lmdatadir}/valid.txt
    ${cuda_cmd} --gpu ${ngpu} ${lmexpdir}/train.log \
        lm_train.py \
        --config ${lm_config} \
        --ngpu ${ngpu} \
        --backend ${backend} \
        --verbose 1 \
        --outdir ${lmexpdir} \
        --tensorboard-dir tensorboard/${lmexpname} \
        --train-label ${lmdatadir}/train.txt \
        --valid-label ${lmdatadir}/valid.txt \
        --resume ${lm_resume} \
        --dict ${dict}
fi


if [ -z ${tag} ]; then
    expname=${train_set}_${backend}_$(basename ${train_config%.*})
    if ${do_delta}; then
        expname=${expname}_delta
    fi
    if [ -n "${preprocess_config}" ]; then
        expname=${expname}_$(basename ${preprocess_config%.*})
    fi
else
    expname=${train_set}_${backend}_${tag}
fi
expdir=exp/${expname}
mkdir -p ${expdir}

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    echo "stage 4: Network Training"
    ${cuda_cmd} --gpu ${ngpu} ${expdir}/train.log \
        asr_train.py \
        --config ${train_config} \
        --preprocess-conf ${preprocess_config} \
        --ngpu ${ngpu} \
        --backend ${backend} \
        --outdir ${expdir}/results \
        --tensorboard-dir tensorboard/${expname} \
        --debugmode ${debugmode} \
        --dict ${dict} \
        --debugdir ${expdir} \
        --minibatches ${N} \
        --verbose ${verbose} \
        --resume ${resume} \
        --train-json ${feat_tr_dir}/data.json \
        --valid-json ${feat_dt_dir}/data.json
fi

if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
    echo "stage 5: Decoding"
    nj=16
    if [[ $(get_yaml.py ${train_config} model-module) = *transformer* ]] || \
           [[ $(get_yaml.py ${train_config} model-module) = *conformer* ]] || \
           [[ $(get_yaml.py ${train_config} etype) = custom ]] || \
           [[ $(get_yaml.py ${train_config} dtype) = custom ]]; then
        recog_model=model.last${n_average}.avg.best
        average_checkpoints.py --backend ${backend} \
                               --snapshots ${expdir}/results/snapshot.ep.* \
                               --out ${expdir}/results/${recog_model} \
                               --num ${n_average}
    fi

    pids=() # initialize pids
    for rtask in ${recog_set}; do
    (
        decode_dir=decode_${rtask}_$(basename ${decode_config%.*})
        feat_recog_dir=${dumpdir}/${rtask}/delta${do_delta}

        # split data
        splitjson.py --parts ${nj} ${feat_recog_dir}/data.json

        #### use CPU for decoding
        ngpu=0

        ${decode_cmd} JOB=1:${nj} ${expdir}/${decode_dir}/log/decode.JOB.log \
            asr_recog.py \
            --config ${decode_config} \
            --ngpu ${ngpu} \
            --backend ${backend} \
            --debugmode ${debugmode} \
            --verbose ${verbose} \
            --recog-json ${feat_recog_dir}/split${nj}utt/data.JOB.json \
            --result-label ${expdir}/${decode_dir}/data.JOB.json \
            --model ${expdir}/results/${recog_model} \
	    --rnnlm ${lmexpdir}/rnnlm.model.best

        score_sclite.sh --wer true ${expdir}/${decode_dir} ${dict}

    ) &
    pids+=($!) # store background pids
    done
    i=0; for pid in "${pids[@]}"; do wait ${pid} || ((++i)); done
    [ ${i} -gt 0 ] && echo "$0: ${i} background jobs are failed." && false
    echo "Finished"
fi
