#!/bin/bash
set -e
source path.sh

stage=0
stop_stage=100
conf_path=conf/deepspeech2.yaml
avg_num=30
model_type=offline
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;

avg_ckpt=avg_${avg_num}
ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
echo "checkpoint name ${ckpt}"

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    # prepare data
    bash ./local/data.sh || exit -1
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    # train model, all `ckpt` under `exp` dir
    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./local/train.sh ${conf_path}  ${ckpt} ${model_type}
fi

if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    # avg n best model
    avg.sh exp/${ckpt}/checkpoints ${avg_num}
fi

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    # test ckpt avg_n
    CUDA_VISIBLE_DEVICES=7 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
fi

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    # export ckpt avg_n
    CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
fi
