diff --git a/examples/hey_snips/s0/run.sh b/examples/hey_snips/s0/run.sh
index d2ac9c7..cb561aa 100755
--- a/examples/hey_snips/s0/run.sh
+++ b/examples/hey_snips/s0/run.sh
@@ -4,8 +4,6 @@
 
 . ./path.sh
 
-export CUDA_VISIBLE_DEVICES="0"
-
 stage=0
 stop_stage=4
 num_keywords=1
@@ -13,7 +11,7 @@ num_keywords=1
 config=conf/ds_tcn.yaml
 norm_mean=true
 norm_var=true
-gpu_id=0
+gpus="0"
 
 checkpoint=
 dir=exp/ds_tcn
@@ -68,7 +66,9 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
   cmvn_opts=
   $norm_mean && cmvn_opts="--cmvn_file data/train/global_cmvn"
   $norm_var && cmvn_opts="$cmvn_opts --norm_var"
-  python kws/bin/train.py --gpu $gpu_id \
+  num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
+  torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
+   kws/bin/train.py --gpus $gpus \
     --config $config \
     --train_data data/train/data.list \
     --cv_data data/dev/data.list \
@@ -82,28 +82,21 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
 fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
-  # Do model average
+  echo "Do model average, Compute FRR/FAR ..."
   python kws/bin/average_model.py \
     --dst_model $score_checkpoint \
     --src_path $dir  \
     --num ${num_average} \
     --val_best
-
-  # Compute posterior score
   result_dir=$dir/test_$(basename $score_checkpoint)
   mkdir -p $result_dir
-  python kws/bin/score.py --gpu $gpu_id \
+  python kws/bin/score.py \
     --config $dir/config.yaml \
     --test_data data/test/data.list \
     --batch_size 256 \
     --checkpoint $score_checkpoint \
     --score_file $result_dir/score.txt \
     --num_workers 8
-fi
-
-if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
-  # Compute detection error tradeoff
-  result_dir=$dir/test_$(basename $score_checkpoint)
   first_keyword=0
   last_keyword=$(($num_keywords+$first_keyword-1))
   for keyword in $(seq $first_keyword $last_keyword); do
@@ -116,10 +109,37 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
 fi
 
 
-if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
-  python kws/bin/export_jit.py --config $dir/config.yaml \
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+  echo "Static quantization, compute FRR/FAR..."
+  # Apply static quantization
+  quantize_score_checkpoint=$(basename $score_checkpoint | sed -e 's:.pt$:.quant.zip:g')
+  cat data/train/data.list | python tools/shuffle_list.py --seed 777 | \
+      head -n 10000 > $dir/calibration.list
+  python kws/bin/static_quantize.py \
+    --config $dir/config.yaml \
+    --test_data $dir/calibration.list \
     --checkpoint $score_checkpoint \
-    --output_file $dir/final.zip \
-    --output_quant_file $dir/final.quant.zip
+    --num_workers 8 \
+    --script_model $dir/$quantize_score_checkpoint
+
+  result_dir=$dir/test_$(basename $quantize_score_checkpoint)
+  mkdir -p $result_dir
+  python kws/bin/score.py \
+    --config $dir/config.yaml \
+    --test_data data/test/data.list \
+    --batch_size 256 \
+    --jit_model \
+    --checkpoint $dir/$quantize_score_checkpoint \
+    --score_file $result_dir/score.txt \
+    --num_workers 8
+  first_keyword=0
+  last_keyword=$(($num_keywords+$first_keyword-1))
+  for keyword in $(seq $first_keyword $last_keyword); do
+    python kws/bin/compute_det.py \
+      --keyword $keyword \
+      --test_data data/test/data.list \
+      --score_file $result_dir/score.txt \
+      --stats_file $result_dir/stats.${keyword}.txt
+  done
 fi