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语帆 2024-02-28 19:30:15 +08:00
parent 59ae516f67
commit 179a3f99c4
1 changed files with 39 additions and 38 deletions
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77
examples/industrial_data_pretraining/lcbnet/demo_nj.sh
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@ -16,46 +16,46 @@ inference_dir="outputs/test"
_logdir="${inference_dir}/logdir"
echo "inference_dir: ${inference_dir}"
mkdir -p "${_logdir}"
key_file1=${file_dir}/wav.scp
key_file2=${file_dir}/ocr.txt
split_scps1=
split_scps2=
for JOB in $(seq "${nj}"); do
split_scps1+=" ${_logdir}/wav.${JOB}.scp"
split_scps2+=" ${_logdir}/ocr.${JOB}.txt"
done
utils/split_scp.pl "${key_file1}" ${split_scps1}
utils/split_scp.pl "${key_file2}" ${split_scps2}
# mkdir -p "${_logdir}"
# key_file1=${file_dir}/wav.scp
# key_file2=${file_dir}/ocr.txt
# split_scps1=
# split_scps2=
# for JOB in $(seq "${nj}"); do
# split_scps1+=" ${_logdir}/wav.${JOB}.scp"
# split_scps2+=" ${_logdir}/ocr.${JOB}.txt"
# done
# utils/split_scp.pl "${key_file1}" ${split_scps1}
# utils/split_scp.pl "${key_file2}" ${split_scps2}
gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
for JOB in $(seq ${nj}); do
{
id=$((JOB-1))
gpuid=${gpuid_list_array[$id]}
# gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
# for JOB in $(seq ${nj}); do
# {
# id=$((JOB-1))
# gpuid=${gpuid_list_array[$id]}
export CUDA_VISIBLE_DEVICES=${gpuid}
# export CUDA_VISIBLE_DEVICES=${gpuid}
python -m funasr.bin.inference \
--config-path=${file_dir} \
--config-name="config.yaml" \
++init_param=${file_dir}/model.pb \
++tokenizer_conf.token_list=${file_dir}/tokens.txt \
++input=[${_logdir}/wav.${JOB}.scp,${_logdir}/ocr.${JOB}.txt] \
+data_type='["kaldi_ark", "text"]' \
++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
++output_dir="${inference_dir}/${JOB}" \
++device="${inference_device}" \
++ncpu=1 \
++disable_log=true &> ${_logdir}/log.${JOB}.txt
# python -m funasr.bin.inference \
# --config-path=${file_dir} \
# --config-name="config.yaml" \
# ++init_param=${file_dir}/model.pb \
# ++tokenizer_conf.token_list=${file_dir}/tokens.txt \
# ++input=[${_logdir}/wav.${JOB}.scp,${_logdir}/ocr.${JOB}.txt] \
# +data_type='["kaldi_ark", "text"]' \
# ++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
# ++output_dir="${inference_dir}/${JOB}" \
# ++device="${inference_device}" \
# ++ncpu=1 \
# ++disable_log=true &> ${_logdir}/log.${JOB}.txt
}&
done
wait
# }&
# done
# wait
mkdir -p ${inference_dir}/1best_recog
for f in token score text; do
#mkdir -p ${inference_dir}/1best_recog
for f in token; do
if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
for JOB in $(seq "${nj}"); do
cat "${inference_dir}/${JOB}/1best_recog/${f}"
@ -65,7 +65,8 @@ done
echo "Computing WER ..."
echo "Computing WER ..."
python utils/postprocess_text_zh.py ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
python utils/postprocess_text_zh.py ${data_dir}/text ${inference_dir}/1best_recog/text.ref
python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
tail -n 3 ${inference_dir}/1best_recog/text.cer
#python utils/postprocess_text_zh.py ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
#cp ${data_dir}/text ${inference_dir}/1best_recog/text.ref
#python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
#tail -n 3 ${inference_dir}/1best_recog/text.cer