test

examples/industrial_data_pretraining/lcbnet/demo_nj.sh

@@ -1,13 +1,71 @@
 file_dir="/nfs/yufan.yf/workspace/github/FunASR/examples/industrial_data_pretraining/lcbnet/exp/speech_lcbnet_contextual_asr-en-16k-bpe-vocab5002-pytorch"
+CUDA_VISIBLE_DEVICES="0,1"
+inference_device="cuda"
 
-#CUDA_VISIBLE_DEVICES="" \
-python -m funasr.bin.inference \
---config-path=${file_dir} \
---config-name="config.yaml" \
-++init_param=${file_dir}/model.pb \
-++tokenizer_conf.token_list=${file_dir}/tokens.txt \
-++input=[${file_dir}/wav.scp,${file_dir}/ocr.txt] \
-+data_type='["kaldi_ark", "text"]' \
-++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
-++output_dir="./outputs/debug" \
-++device="cpu" \
+if [ ${inference_device} == "cuda" ]; then
+    nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+else
+    inference_batch_size=1
+    CUDA_VISIBLE_DEVICES=""
+    for JOB in $(seq ${nj}); do
+        CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
+    done
+fi
+
+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}" ${split_scps2}
+
+gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
+for JOB in $(seq ${nj}); do
+    {
+        id=$((JOB-1))
+        gpuid=${gpuid_list_array[$id]}
+
+        export CUDA_VISIBLE_DEVICES=${gpuid}
+
+        python -m funasr.bin.inference \
+        --config-path=${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
+
+
+mkdir -p ${inference_dir}/1best_recog
+for f in token score text; do
+    if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
+        for JOB in $(seq "${nj}"); do
+            cat "${inference_dir}/${JOB}/1best_recog/${f}"
+        done | sort -k1 >"${inference_dir}/1best_recog/${f}"
+    fi
+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