| New file |
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| | | file_dir="/nfs/yufan.yf/workspace/github/FunASR/examples/industrial_data_pretraining/lcbnet/exp/speech_lcbnet_contextual_asr-en-16k-bpe-vocab5002-pytorch" |
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| | | CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" |
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| | | inference_device="cuda" |
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| | | |
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| | | if [ ${inference_device} == "cuda" ]; then |
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| | | nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') |
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| | | else |
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| | | inference_batch_size=1 |
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| | | CUDA_VISIBLE_DEVICES="" |
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| | | for JOB in $(seq ${nj}); do |
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| | | CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1," |
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| | | done |
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| | | fi |
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| | | |
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| | | inference_dir="outputs/test" |
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| | | _logdir="${inference_dir}/logdir" |
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| | | echo "inference_dir: ${inference_dir}" |
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| | | |
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| | | # mkdir -p "${_logdir}" |
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| | | # key_file1=${file_dir}/wav.scp |
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| | | # key_file2=${file_dir}/ocr.txt |
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| | | # split_scps1= |
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| | | # split_scps2= |
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| | | # for JOB in $(seq "${nj}"); do |
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| | | # split_scps1+=" ${_logdir}/wav.${JOB}.scp" |
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| | | # split_scps2+=" ${_logdir}/ocr.${JOB}.txt" |
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| | | # done |
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| | | # utils/split_scp.pl "${key_file1}" ${split_scps1} |
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| | | # utils/split_scp.pl "${key_file2}" ${split_scps2} |
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| | | |
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| | | # gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ }) |
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| | | # for JOB in $(seq ${nj}); do |
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| | | # { |
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| | | # id=$((JOB-1)) |
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| | | # gpuid=${gpuid_list_array[$id]} |
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| | | |
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| | | # export CUDA_VISIBLE_DEVICES=${gpuid} |
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| | | |
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| | | # python -m funasr.bin.inference \ |
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| | | # --config-path=${file_dir} \ |
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| | | # --config-name="config.yaml" \ |
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| | | # ++init_param=${file_dir}/model.pb \ |
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| | | # ++tokenizer_conf.token_list=${file_dir}/tokens.txt \ |
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| | | # ++input=[${_logdir}/wav.${JOB}.scp,${_logdir}/ocr.${JOB}.txt] \ |
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| | | # +data_type='["kaldi_ark", "text"]' \ |
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| | | # ++tokenizer_conf.bpemodel=${file_dir}/bpe.model \ |
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| | | # ++output_dir="${inference_dir}/${JOB}" \ |
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| | | # ++device="${inference_device}" \ |
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| | | # ++ncpu=1 \ |
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| | | # ++disable_log=true &> ${_logdir}/log.${JOB}.txt |
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| | | |
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| | | # }& |
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| | | # done |
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| | | # wait |
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| | | |
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| | | |
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| | | #mkdir -p ${inference_dir}/1best_recog |
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| | | for f in token; do |
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| | | if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then |
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| | | for JOB in $(seq "${nj}"); do |
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| | | cat "${inference_dir}/${JOB}/1best_recog/${f}" |
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| | | done | sort -k1 >"${inference_dir}/1best_recog/${f}" |
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| | | fi |
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| | | done |
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| | | |
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| | | echo "Computing WER ..." |
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| | | echo "Computing WER ..." |
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| | | #python utils/postprocess_text_zh.py ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc |
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| | | |
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| | | #cp ${data_dir}/text ${inference_dir}/1best_recog/text.ref |
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| | | #python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer |
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| | | #tail -n 3 ${inference_dir}/1best_recog/text.cer |
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