[wekws] rename kws to wekws (#76)

* [wekws] rename kws to wekws

* fix lint

examples/hey_snips/s0/kws

@@ -1 +0,0 @@
-../../../kws

examples/hey_snips/s0/run.sh

@@ -68,7 +68,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
   $norm_var && cmvn_opts="$cmvn_opts --norm_var"
   num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
   torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
-   kws/bin/train.py --gpus $gpus \
+   wekws/bin/train.py --gpus $gpus \
     --config $config \
     --train_data data/train/data.list \
     --cv_data data/dev/data.list \
@@ -83,14 +83,14 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
   echo "Do model average, Compute FRR/FAR ..."
-  python kws/bin/average_model.py \
+  python wekws/bin/average_model.py \
     --dst_model $score_checkpoint \
     --src_path $dir  \
     --num ${num_average} \
     --val_best
   result_dir=$dir/test_$(basename $score_checkpoint)
   mkdir -p $result_dir
-  python kws/bin/score.py \
+  python wekws/bin/score.py \
     --config $dir/config.yaml \
     --test_data data/test/data.list \
     --batch_size 256 \
@@ -100,13 +100,13 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
   first_keyword=0
   last_keyword=$(($num_keywords+$first_keyword-1))
   for keyword in $(seq $first_keyword $last_keyword); do
-    python kws/bin/compute_det.py \
+    python wekws/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
-  python kws/bin/plot_det_curve.py \
+  python wekws/bin/plot_det_curve.py \
     --keywords_dict dict/words.txt \
     --stats_dir $result_dir \
     --figure_file $result_dir/det.png \
@@ -118,7 +118,7 @@ fi
 
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
-  python kws/bin/export_jit.py --config $dir/config.yaml \
+  python wekws/bin/export_jit.py --config $dir/config.yaml \
     --checkpoint $score_checkpoint \
     --output_file $dir/final.zip \
     --output_quant_file $dir/final.quant.zip

examples/hey_snips/s0/wekws

@@ -0,0 +1 @@
+../../../wekws

examples/hi_xiaowen/s0/kws

@@ -1 +0,0 @@
-../../../kws

examples/hi_xiaowen/s0/run.sh

@@ -73,7 +73,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
   $norm_var && cmvn_opts="$cmvn_opts --norm_var"
   num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
   torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
-    kws/bin/train.py --gpus $gpus \
+    wekws/bin/train.py --gpus $gpus \
       --config $config \
       --train_data data/train/data.list \
       --cv_data data/dev/data.list \
@@ -88,14 +88,14 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
   echo "Do model average, Compute FRR/FAR ..."
-  python kws/bin/average_model.py \
+  python wekws/bin/average_model.py \
     --dst_model $score_checkpoint \
     --src_path $dir  \
     --num ${num_average} \
     --val_best
   result_dir=$dir/test_$(basename $score_checkpoint)
   mkdir -p $result_dir
-  python kws/bin/score.py \
+  python wekws/bin/score.py \
     --config $dir/config.yaml \
     --test_data data/test/data.list \
     --batch_size 256 \
@@ -104,7 +104,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     --num_workers 8
 
   for keyword in 0 1; do
-    python kws/bin/compute_det.py \
+    python wekws/bin/compute_det.py \
       --keyword $keyword \
       --test_data data/test/data.list \
       --window_shift $window_shift \
@@ -120,7 +120,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
   quantize_score_checkpoint=$(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 \
+  python wekws/bin/static_quantize.py \
     --config $dir/config.yaml \
     --test_data $dir/calibration.list \
     --checkpoint $score_checkpoint \
@@ -129,7 +129,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
 
   result_dir=$dir/test_$(basename $quantize_score_checkpoint)
   mkdir -p $result_dir
-  python kws/bin/score.py \
+  python wekws/bin/score.py \
     --config $dir/config.yaml \
     --test_data data/test/data.list \
     --batch_size 256 \
@@ -138,7 +138,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     --score_file $result_dir/score.txt \
     --num_workers 8
   for keyword in 0 1; do
-    python kws/bin/compute_det.py \
+    python wekws/bin/compute_det.py \
       --keyword $keyword \
       --test_data data/test/data.list \
       --score_file $result_dir/score.txt \
@@ -150,12 +150,12 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
   jit_model=$(basename $score_checkpoint | sed -e 's:.pt$:.zip:g')
   onnx_model=$(basename $score_checkpoint | sed -e 's:.pt$:.onnx:g')
-  python kws/bin/export_jit.py \
+  python wekws/bin/export_jit.py \
     --config $dir/config.yaml \
     --checkpoint $score_checkpoint \
     --jit_model $dir/$jit_model
-  python kws/bin/export_onnx.py \
+  python wekws/bin/export_onnx.py \
     --config $dir/config.yaml \
     --checkpoint $score_checkpoint \
     --onnx_model $dir/$onnx_model
-fi
+fi

examples/hi_xiaowen/s0/wekws

@@ -0,0 +1 @@
+../../../wekws

examples/speechcommand_v1/s0/kws

@@ -1 +0,0 @@
-../../../kws

examples/speechcommand_v1/s0/run.sh

@@ -68,7 +68,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
   $norm_var && cmvn_opts="$cmvn_opts --norm_var"
   num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
   torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
-   kws/bin/train.py --gpus $gpus \
+   wekws/bin/train.py --gpus $gpus \
     --config $config \
     --train_data data/train/data.list \
     --cv_data data/valid/data.list \
@@ -82,7 +82,7 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
   # Do model average
-  python kws/bin/average_model.py \
+  python wekws/bin/average_model.py \
     --dst_model $score_checkpoint \
     --src_path $dir  \
     --num ${num_average} \
@@ -91,7 +91,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
   # Testing
   result_dir=$dir/test_$(basename $score_checkpoint)
   mkdir -p $result_dir
-  python kws/bin/compute_accuracy.py --gpu 3 \
+  python wekws/bin/compute_accuracy.py --gpu 3 \
     --config $dir/config.yaml \
     --test_data data/test/data.list \
     --batch_size 256 \
@@ -101,7 +101,7 @@ fi
 
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
-  python kws/bin/export_jit.py --config $dir/config.yaml \
+  python wekws/bin/export_jit.py --config $dir/config.yaml \
     --checkpoint $score_checkpoint \
     --output_file $dir/final.zip \
     --output_quant_file $dir/final.quant.zip

examples/speechcommand_v1/s0/wekws

@@ -0,0 +1 @@
+../../../wekws

wekws/bin/compute_accuracy.py

@@ -23,10 +23,10 @@ import torch
 import yaml
 from torch.utils.data import DataLoader
 
-from kws.dataset.dataset import Dataset
-from kws.model.kws_model import init_model
-from kws.utils.checkpoint import load_checkpoint
-from kws.utils.executor import Executor
+from wekws.dataset.dataset import Dataset
+from wekws.model.kws_model import init_model
+from wekws.utils.checkpoint import load_checkpoint
+from wekws.utils.executor import Executor
 
 
 def get_args():

wekws/bin/export_jit.py

@@ -20,8 +20,8 @@ import os
 import torch
 import yaml
 
-from kws.model.kws_model import init_model
-from kws.utils.checkpoint import load_checkpoint
+from wekws.model.kws_model import init_model
+from wekws.utils.checkpoint import load_checkpoint
 
 
 def get_args():

wekws/bin/export_onnx.py

@@ -18,8 +18,8 @@ import torch
 import yaml
 import onnxruntime as ort
 
-from kws.model.kws_model import init_model
-from kws.utils.checkpoint import load_checkpoint
+from wekws.model.kws_model import init_model
+from wekws.utils.checkpoint import load_checkpoint
 
 
 def get_args():

wekws/bin/score.py

@@ -25,9 +25,9 @@ import torch
 import yaml
 from torch.utils.data import DataLoader
 
-from kws.dataset.dataset import Dataset
-from kws.model.kws_model import init_model
-from kws.utils.checkpoint import load_checkpoint
+from wekws.dataset.dataset import Dataset
+from wekws.model.kws_model import init_model
+from wekws.utils.checkpoint import load_checkpoint
 
 
 def get_args():

wekws/bin/static_quantize.py

@@ -24,9 +24,9 @@ import torch
 import yaml
 from torch.utils.data import DataLoader
 
-from kws.dataset.dataset import Dataset
-from kws.model.kws_model import init_model
-from kws.utils.checkpoint import load_checkpoint
+from wekws.dataset.dataset import Dataset
+from wekws.model.kws_model import init_model
+from wekws.utils.checkpoint import load_checkpoint
 
 
 def get_args():

wekws/bin/train.py

@@ -26,11 +26,11 @@ import yaml
 from tensorboardX import SummaryWriter
 from torch.utils.data import DataLoader
 
-from kws.dataset.dataset import Dataset
-from kws.utils.checkpoint import load_checkpoint, save_checkpoint
-from kws.model.kws_model import init_model
-from kws.utils.executor import Executor
-from kws.utils.train_utils import count_parameters, set_mannul_seed
+from wekws.dataset.dataset import Dataset
+from wekws.utils.checkpoint import load_checkpoint, save_checkpoint
+from wekws.model.kws_model import init_model
+from wekws.utils.executor import Executor
+from wekws.utils.train_utils import count_parameters, set_mannul_seed
 
 
 def get_args():

wekws/dataset/dataset.py

@@ -18,8 +18,8 @@ import torch
 import torch.distributed as dist
 from torch.utils.data import IterableDataset
 
-import kws.dataset.processor as processor
-from kws.utils.file_utils import read_lists
+import wekws.dataset.processor as processor
+from wekws.utils.file_utils import read_lists
 
 
 class Processor(IterableDataset):

wekws/model/kws_model.py

@@ -18,12 +18,14 @@ from typing import Optional
 import torch
 import torch.nn as nn
 
-from kws.model.cmvn import GlobalCMVN
-from kws.model.classifier import GlobalClassifier, LastClassifier, LinearClassifier
-from kws.model.subsampling import LinearSubsampling1, Conv1dSubsampling1, NoSubsampling
-from kws.model.tcn import TCN, CnnBlock, DsCnnBlock
-from kws.model.mdtc import MDTC
-from kws.utils.cmvn import load_cmvn
+from wekws.model.cmvn import GlobalCMVN
+from wekws.model.classifier import (GlobalClassifier, LastClassifier,
+                                    LinearClassifier)
+from wekws.model.subsampling import (LinearSubsampling1, Conv1dSubsampling1,
+                                     NoSubsampling)
+from wekws.model.tcn import TCN, CnnBlock, DsCnnBlock
+from wekws.model.mdtc import MDTC
+from wekws.utils.cmvn import load_cmvn
 
 
 class KWSModel(nn.Module):

wekws/model/loss.py

@@ -15,7 +15,7 @@
 import torch
 import torch.nn.functional as F
 
-from kws.utils.mask import padding_mask
+from wekws.utils.mask import padding_mask
 
 
 def max_pooling_loss(logits: torch.Tensor,

wekws/utils/executor.py

@@ -17,7 +17,7 @@ import logging
 import torch
 from torch.nn.utils import clip_grad_norm_
 
-from kws.model.loss import criterion
+from wekws.model.loss import criterion
 
 
 class Executor: