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test
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语帆
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examples/industrial_data_pretraining/lcbnet/demo_nj.sh
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72
examples/industrial_data_pretraining/lcbnet/demo_nj.sh
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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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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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inference_dir="outputs/test"
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_logdir="${inference_dir}/logdir"
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echo "inference_dir: ${inference_dir}"
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# mkdir -p "${_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}" ${split_scps2}
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# gpuid_list_array=(${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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# export CUDA_VISIBLE_DEVICES=${gpuid}
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# python -m funasr.bin.inference \
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# --config-path=${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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# done
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# wait
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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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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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#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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@ -1,72 +0,0 @@
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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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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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inference_dir="outputs/test"
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_logdir="${inference_dir}/logdir"
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echo "inference_dir: ${inference_dir}"
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# mkdir -p "${_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}" ${split_scps2}
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# gpuid_list_array=(${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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# export CUDA_VISIBLE_DEVICES=${gpuid}
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# python -m funasr.bin.inference \
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# --config-path=${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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# done
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# wait
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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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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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#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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