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80 lines
1.7 KiB
80 lines
1.7 KiB
#!/bin/bash
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if [ $# != 3 ];then
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echo "usage: ${0} config_path dict_path ckpt_path_prefix"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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device=gpu
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if [ ${ngpu} == 0 ];then
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device=cpu
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fi
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config_path=$1
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dict_path=$2
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ckpt_prefix=$3
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chunk_mode=false
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if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
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chunk_mode=true
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fi
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echo "chunk mode ${chunk_mode}"
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# download language model
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#bash local/download_lm_en.sh
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#if [ $? -ne 0 ]; then
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# exit 1
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#fi
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for type in attention ctc_greedy_search; do
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echo "decoding ${type}"
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if [ ${chunk_mode} == true ];then
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# stream decoding only support batchsize=1
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batch_size=1
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else
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batch_size=64
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fi
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python3 -u ${BIN_DIR}/test.py \
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--model-name u2_kaldi \
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--run-mode test \
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--dict-path ${dict_path} \
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--device ${device} \
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--nproc 1 \
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--config ${config_path} \
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--result-file ${ckpt_prefix}.${type}.rsl \
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--checkpoint_path ${ckpt_prefix} \
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--opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
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if [ $? -ne 0 ]; then
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echo "Failed in evaluation!"
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exit 1
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fi
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done
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for type in ctc_prefix_beam_search attention_rescoring; do
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echo "decoding ${type}"
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batch_size=1
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python3 -u ${BIN_DIR}/test.py \
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--model-name u2_kaldi \
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--run-mode test \
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--dict-path ${dict_path} \
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--device ${device} \
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--nproc 1 \
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--config ${config_path} \
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--result-file ${ckpt_prefix}.${type}.rsl \
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--checkpoint_path ${ckpt_prefix} \
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--opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
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if [ $? -ne 0 ]; then
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echo "Failed in evaluation!"
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exit 1
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fi
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done
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exit 0
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