Add KWS example.

examples/hey_snips/kws0/local/train.sh

@@ -7,6 +7,7 @@ if [ ${ngpu} -gt 0 ]; then
     python3 -m paddle.distributed.launch --gpus $CUDA_VISIBLE_DEVICES ${BIN_DIR}/train.py \
     --cfg_path ${cfg_path}
 else
+    echo "set CUDA_VISIBLE_DEVICES to enable multi-gpus trainning."
     python3 ${BIN_DIR}/train.py \
     --cfg_path ${cfg_path}
 fi

examples/hey_snips/kws0/run.sh

@@ -18,6 +18,11 @@ source path.sh
 
 ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
+if [ $# != 1 ];then
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path"
+    exit -1
+fi
+
 stage=1
 stop_stage=3
 

paddlespeech/kws/exps/mdtc/compute_det.py

@@ -15,6 +15,7 @@
 import argparse
 import os
 
+import paddle
 import yaml
 from tqdm import tqdm
 
@@ -23,32 +24,34 @@ from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--cfg_path", type=str, required=True)
-parser.add_argument('--keyword', type=int, default=0, help='keyword label')
+parser.add_argument('--keyword_index', type=int, default=0, help='keyword index')
-parser.add_argument('--step', type=float, default=0.01, help='threshold step')
+parser.add_argument('--step', type=float, default=0.01, help='threshold step of trigger score')
 parser.add_argument('--window_shift', type=int, default=50, help='window_shift is used to skip the frames after triggered')
 args = parser.parse_args()
 # yapf: enable
 
 
-def load_label_and_score(keyword, ds, score_file):
+def load_label_and_score(keyword_index: int,
-    score_table = {}
+                         ds: paddle.io.Dataset,
+                         score_file: os.PathLike):
+    score_table = {}  # {utt_id: scores_over_frames}
     with open(score_file, 'r', encoding='utf8') as fin:
         for line in fin:
             arr = line.strip().split()
             key = arr[0]
             current_keyword = arr[1]
             str_list = arr[2:]
-            if int(current_keyword) == keyword:
+            if int(current_keyword) == keyword_index:
                 scores = list(map(float, str_list))
                 if key not in score_table:
                     score_table.update({key: scores})
-    keyword_table = {}
+    keyword_table = {}  # scores of keyword utt_id
-    filler_table = {}
+    filler_table = {}  # scores of non-keyword utt_id
     filler_duration = 0.0
 
     for key, index, duration in zip(ds.keys, ds.labels, ds.durations):
         assert key in score_table
-        if index == keyword:
+        if index == keyword_index:
             keyword_table[key] = score_table[key]
         else:
             filler_table[key] = score_table[key]
@@ -78,7 +81,7 @@ if __name__ == '__main__':
     print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
     pbar = tqdm(total=int(1.0 / args.step))
     with open(stats_file, 'w', encoding='utf8') as fout:
-        keyword_index = args.keyword
+        keyword_index = args.keyword_index
         threshold = 0.0
         while threshold <= 1.0:
             num_false_reject = 0

paddlespeech/kws/models/loss.py

@@ -15,7 +15,16 @@
 import paddle
 
 
-def fill_mask_elements(condition, value, x):
+def padding_mask(lengths: paddle.Tensor) -> paddle.Tensor:
+    batch_size = lengths.shape[0]
+    max_len = int(lengths.max().item())
+    seq = paddle.arange(max_len, dtype=paddle.int64)
+    seq = seq.expand((batch_size, max_len))
+    return seq >= lengths.unsqueeze(1)
+
+
+def fill_mask_elements(condition: paddle.Tensor, value: float,
+                       x: paddle.Tensor) -> paddle.Tensor:
     assert condition.shape == x.shape
     values = paddle.ones_like(x, dtype=x.dtype) * value
     return paddle.where(condition, values, x)
@@ -70,11 +79,3 @@ def max_pooling_loss(logits: paddle.Tensor,
     acc = num_correct / num_utts
     # acc = 0.0
     return loss, num_correct, acc
-
-
-def padding_mask(lengths: paddle.Tensor) -> paddle.Tensor:
-    batch_size = lengths.shape[0]
-    max_len = int(lengths.max().item())
-    seq = paddle.arange(max_len, dtype=paddle.int64)
-    seq = seq.expand((batch_size, max_len))
-    return seq >= lengths.unsqueeze(1)