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#!/bin/bash
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set -e
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source path.sh
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stage=0
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stop_stage=100
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conf_path=conf/transformer.yaml
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dict_path=data/train_960_unigram5000_units.txt
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avg_num=5
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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avg_ckpt=avg_${avg_num}
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ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
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echo "checkpoint name ${ckpt}"
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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bash ./local/data.sh || exit -1
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `exp` dir
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CUDA_VISIBLE_DEVICES=0,1,2,3 ./local/train.sh ${conf_path} ${ckpt}
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# avg n best model
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avg.sh best exp/${ckpt}/checkpoints ${avg_num}
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# test ckpt avg_n
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CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# ctc alignment of test data
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CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
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fi
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# export ckpt avg_n
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CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
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fi
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