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103 lines
2.6 KiB
103 lines
2.6 KiB
#!/bin/bash
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set -e
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expdir=exp
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datadir=data
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nj=32
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lmtag='nolm'
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train_set=train_960
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recog_set="test-clean test-other dev-clean dev-other"
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recog_set="test-clean"
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# bpemode (unigram or bpe)
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nbpe=5000
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bpemode=unigram
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bpeprefix=data/lang_char/${train_set}_${bpemode}${nbpe}
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bpemodel=${bpeprefix}.model
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config_path=conf/transformer.yaml
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decode_config_path=conf/decode/decode_base.yaml
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dict=data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
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ckpt_prefix=
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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if [ -z ${ckpt_prefix} ]; then
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echo "usage: $0 --ckpt_prefix ckpt_prefix"
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exit 1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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ckpt_dir=$(dirname `dirname ${ckpt_prefix}`)
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echo "ckpt dir: ${ckpt_dir}"
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ckpt_tag=$(basename ${ckpt_prefix})
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echo "ckpt tag: ${ckpt_tag}"
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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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pids=() # initialize pids
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for dmethd in attention ctc_greedy_search ctc_prefix_beam_search attention_rescoring; do
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(
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echo "decode method: ${dmethd}"
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for rtask in ${recog_set}; do
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(
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echo "dataset: ${rtask}"
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decode_dir=${ckpt_dir}/decode/decode_${rtask/-/_}_${dmethd}_$(basename ${config_path%.*})_${lmtag}_${ckpt_tag}
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feat_recog_dir=${datadir}
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mkdir -p ${decode_dir}
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mkdir -p ${feat_recog_dir}
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# split data
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split_json.sh manifest.${rtask} ${nj}
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#### use CPU for decoding
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ngpu=0
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# set batchsize 0 to disable batch decoding
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batch_size=1
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${decode_cmd} JOB=1:${nj} ${decode_dir}/log/decode.JOB.log \
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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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--ngpu ${ngpu} \
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--dict-path ${dict} \
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--config ${config_path} \
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--decode_cfg ${decode_config_path} \
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--checkpoint_path ${ckpt_prefix} \
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--result-file ${decode_dir}/data.JOB.json \
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--opts decode.decoding_method ${dmethd} \
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--opts decode.decode_batch_size ${batch_size} \
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--opts test_manifest ${feat_recog_dir}/split${nj}/JOB/manifest.${rtask}
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score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel} --wer false ${decode_dir} ${dict}
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) &
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pids+=($!) # store background pids
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i=0; for pid in "${pids[@]}"; do wait ${pid} || ((++i)); done
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[ ${i} -gt 0 ] && echo "$0: ${i} background jobs are failed." || true
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done
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)
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done
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echo "Finished"
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exit 0
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