#!/usr/bin/env bash

# Copyright 2019 IIIT-Bangalore (Shreekantha Nadig)
#  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=1      # verbose option
resume=        # Resume the training from snapshot

# feature configuration
do_delta=false

train_config=conf/train.yaml
decode_config=conf/decode.yaml

# decoding parameter
recog_model=model.acc.best # set a model to be used for decoding: 'model.acc.best' or 'model.loss.best'

# data
timit=/home/shree/TIMIT
trans_type=char

# 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=train_nodev
train_dev=train_dev
recog_set="train_dev test"

if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
    local/timit_data_prep.sh ${timit} ${trans_type} || exit 1
fi

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    local/timit_format_data.sh
fi

feat_tr_dir=${dumpdir}/${train_set}/delta${do_delta}; mkdir -p ${feat_tr_dir}
feat_dt_dir=${dumpdir}/${train_dev}/delta${do_delta}; mkdir -p ${feat_dt_dir}
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    ### Task dependent. You have to design training and dev sets by yourself.
    ### But you can utilize Kaldi recipes in most cases
    echo "stage 1: Feature Generation"
    fbankdir=fbank
    # Generate the fbank features; by default 80-dimensional fbanks with pitch on each frame
    for x in test train dev; do
        steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj 8 --write_utt2num_frames true \
        data/${x} exp/make_fbank/${x} ${fbankdir}
    done

    # make a dev set
    # Move train and dev folders (kaldi style naming) to train_nodev and train_dev
    mv data/train data/${train_set}
    mv data/dev data/${train_dev}

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

    # dump features
    dump.sh --cmd "$train_cmd" --nj 8 --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 8 --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 8 --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/${train_set}_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" > ${dict} # <unk> must be 1, 0 will be used for "blank" in CTC
    text2token.py -s 1 -n 1 data/${train_set}/text --trans_type ${trans_type} | cut -f 2- -d" " | tr " " "\n" \
    | sort | uniq | grep -v -e '^\s*$' | awk '{print $0 " " NR+1}' >> ${dict}
    wc -l ${dict}

    # make json labels
    data2json.sh --feat ${feat_tr_dir}/feats.scp --trans_type ${trans_type} \
    data/${train_set} ${dict} > ${feat_tr_dir}/data.json
    data2json.sh --feat ${feat_dt_dir}/feats.scp --trans_type ${trans_type} \
    data/${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 --trans_type ${trans_type} \
        data/${rtask} ${dict} > ${feat_recog_dir}/data.json
    done
fi

if [ -z ${tag} ]; then
    expname=${train_set}_${backend}_$(basename ${train_config%.*})
    if ${do_delta}; then
        expname=${expname}_delta
    fi
else
    expname=${train_set}_${backend}_${tag}
fi
expdir=exp/${expname}
mkdir -p ${expdir}
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    echo "stage 3: Network Training"
    ${cuda_cmd} --gpu ${ngpu} ${expdir}/train.log \
    asr_train.py \
    --config ${train_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 4 ] && [ ${stop_stage} -ge 4 ]; then
    echo "stage 4: Decoding"
    nj=8
    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}

            score_sclite.sh ${expdir}/${decode_dir} ${dict}

        ) &
    done
    wait
    echo "Finished"
fi
