update comments

egs/himia/wuw/README.md

@@ -0,0 +1,10 @@
+# Pretrained models and related logs/results.
+
+## ctc tdnn baseline
+
+AUC results for different epochs could be found at <https://huggingface.co/GuoLiyong/himia_ctc_tdnn_baseline>
+
+E.g. for epoch 15 and avg 1, result log file is: <https://huggingface.co/GuoLiyong/himia_ctc_tdnn_baseline/blob/main/exp_max_duration_100/post/epoch_15-avg_1/log/log-auc-himia_aishell-2023-03-16-16-02-56>
+
+Corresponding ROC curve is: <https://huggingface.co/GuoLiyong/himia_ctc_tdnn_baseline/blob/main/exp_max_duration_100/post/epoch_15-avg_1/himia_aishell.pdf>
+

egs/himia/wuw/ctc_tdnn/README.md

@@ -1,10 +0,0 @@
-# Pretrained models and releated logs/results.
-
-## ctc tdnn baseline
-
-Auc results for different epochs could be found at <https://huggingface.co/GuoLiyong/himia_ctc_tdnn_baseline>
-
-E.g. for epoch 2 and avg 1, auc log file is: <https://huggingface.co/GuoLiyong/himia_ctc_tdnn_baseline/blob/main/exp_max_duration_100/post/epoch_2-avg_1/log/log-auc-himia_aishell-2023-03-16-16-02-56>
-
-Corresponding ROC curve is: <https://huggingface.co/GuoLiyong/himia_ctc_tdnn_baseline/blob/main/exp_max_duration_100/post/epoch_2-avg_1/himia_aishell.pdf>
-

egs/himia/wuw/ctc_tdnn/decode.py

@@ -61,28 +61,49 @@ class FiniteStateTransducer:
     """Represents a decoding graph for wake word detection."""
 
     def __init__(self, graph: str) -> None:
+        """
+        Construct a decoding graph in FST format given string format graph.
+
+        Args:
+          graph: A string format fst. Each arc is separated by "\n".
+        """
         self.state_list = list()
         for arc_str in graph.split("\n"):
             arc = arc_str.strip().split()
             if len(arc) == 0:
                 continue
+            # An arc may contain 1, 2 or 4 elements, with format:
+            # src_state [dst_state] [ilabel] [olabel]
             # 1 and 2 for final state
             # 4 for non-final state
             assert len(arc) in [1, 2, 4], f"{len(arc)} {arc_str}"
+            arc = [int(element) for element in arc]
+            src_state_id = arc[0]
+            max_state_id = len(self.state_list) - 1
             if len(arc) == 4:  # Non-final state
-                # FST must be sorted
+                assert max_state_id <= src_state_id, (
-                if len(self.state_list) <= int(arc[0]):
+                    f"Fsa must be sorted by src_state, "
+                    f"while {cur_number_states} <= {src_state_id}. Check your graph."
+                )
+                if max_state_id < src_state_id:
                     new_state = State()
                     self.state_list.append(new_state)
-                self.state_list[int(arc[0])].add_arc(
+
-                    Arc(arc[0], arc[1], arc[2], arc[3])
+                self.state_list[src_state_id].add_arc(
+                    Arc(src_state_id, arc[1], arc[2], arc[3])
                 )
             else:
-                self.final_state_id = int(arc[0])
+                assert (
+                    max_state_id == src_state_id
+                ), f"Final state seems unreachable. Check your graph."
+                self.final_state_id = src_state_id
 
     def to_str(self) -> None:
         fst_str = ""
-        for state_idx in range(len(self.state_list)):
+        number_states = len(self.state_list)
+        if number_states == 0:
+            return fst_str
+        for state_idx in range(number_states):
             cur_state = self.state_list[state_idx]
             for arc_idx in range(len(cur_state.arc_list)):
                 cur_arc = cur_state.arc_list[arc_idx]

egs/himia/wuw/ctc_tdnn/graph.py

@@ -21,7 +21,7 @@ from typing import List
 
 def ctc_trivial_decoding_graph(wakeup_word_tokens: List[int]):
     """
-    A graph starts with blank/unknown and follwoing by wakeup word.
+    A graph starts with blank/unknown and following by wakeup word.
 
     Args:
       wakeup_word_tokens: A sequence of token ids corresponding wakeup_word.

egs/himia/wuw/ctc_tdnn/inference.py

@@ -69,7 +69,7 @@ def get_params() -> AttributeDict:
         {
             "env_info": get_env_info(),
             "feature_dim": 80,
-            "number_class": 9,
+            "num_class": 9,
         }
     )
     return params
@@ -150,7 +150,7 @@ def main():
 
     logging.info(f"device: {device}")
 
-    model = Tdnn(params.feature_dim, params.number_class)
+    model = Tdnn(params.feature_dim, params.num_class)
 
     if params.avg == 1:
         load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=True)

egs/himia/wuw/ctc_tdnn/tdnn.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-# Copyright    2023  Xiaomi Corp.        (authors: Liyong Guo)
+# Copyright    2023  Xiaomi Corp.        (Author: Liyong Guo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #

egs/himia/wuw/ctc_tdnn/tokenizer.py

@@ -1,4 +1,4 @@
-# Copyright      2023  Xiaomi Corp.        (authors: Liyong Guo)
+# Copyright      2023  Xiaomi Corp.        (Author: Liyong Guo)
 #
 # See ../../LICENSE for clarification regarding multiple authors
 #

egs/himia/wuw/ctc_tdnn/train.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-# Copyright    2023  Xiaomi Corp.        (authors: Liyong Guo)
+# Copyright    2023  Xiaomi Corp.        (Author: Liyong Guo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -162,7 +162,7 @@ def get_params() -> AttributeDict:
         - feature_dim: The model input dim. It has to match the one used
                        in computing features.
 
-        - number_class: Numer of classes. Each token will have a token id
+        - num_class: Number of classes. Each token will have a token id
                      from [0, num_class).
                      In this recipe, 0 is usually kept for blank,
                      and 1 is usually kept for negative words.
@@ -182,7 +182,7 @@ def get_params() -> AttributeDict:
             "valid_interval": 3000,
             # parameters for model
             "feature_dim": 80,
-            "number_class": 9,
+            "num_class": 9,
             # parameters for tokenizer
             "wakeup_word": "你好米雅",
             "wakeup_word_tokens": [2, 3, 4, 5, 6, 3, 7, 8],
@@ -529,7 +529,7 @@ def run(rank, world_size, args):
 
     logging.info("About to create model")
 
-    model = Tdnn(params.feature_dim, params.number_class)
+    model = Tdnn(params.feature_dim, params.numb_class)
 
     checkpoints = load_checkpoint_if_available(params=params, model=model)
 

egs/himia/wuw/local/auc.py

@@ -55,7 +55,7 @@ def load_score(score_file: Path) -> Dict[str, float]:
     """
     Args:
       score_file: Path to score file. Each line has two columns.
-        The first colume is utt-id, and the second one is score.
+        The first column is utt-id, and the second one is score.
         This score could be viewed as probability of being wakeup word.
 
     Returns:
@@ -81,9 +81,9 @@ def get_roc_and_auc(
       pos_dict: scores of positive samples.
       neg_dict: scores of negative samples.
     Return:
-      A tuple of three elements, which will be used to plot roc curve.
+      A tuple of three elements, which will be used to plot ROC curve.
       Refer to sklearn.metrics.roc_curve for meaning of the first and second elements.
-      The third element is area under the roc curve(AUC).
+      The third element is area under the ROC curve(AUC).
     """
     pos_scores = np.fromiter(pos_dict.values(), dtype=float)
     neg_scores = np.fromiter(neg_dict.values(), dtype=float)

egs/himia/wuw/local/compute_fbank_himia.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-# Copyright    2023  Xiaomi Corp.        (authors: Liyong Guo)
+# Copyright    2023  Xiaomi Corp.        (Author: Liyong Guo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -57,7 +57,7 @@ def get_args():
         "--enable-speed-perturb",
         type=str2bool,
         default=False,
-        help="""channel of trianing set.
+        help="""channel of training set.
         """,
     )
     return parser.parse_args()

egs/himia/wuw/prepare.sh

@@ -8,7 +8,7 @@ stop_stage=6
 # HI_MIA and aishell dataset are used in this experiment.
 # musan dataset is used for data augmentation.
 #
-# For aishell dataset downlading and preparation,
+# For aishell dataset downloading and preparation,
 # refer to icefall/egs/aishell/ASR/prepare.sh.
 #
 # For HI_MIA and HI_MIA_CW dataset,
@@ -177,8 +177,12 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
 
   train_file=data/fbank/cuts_train_himia${train_set_channel}-aishell-shuf.jsonl.gz
   if [ ! -f ${train_file} ]; then
-    # SingleCutSampler is prefered for this experiment.
+    # SingleCutSampler is preferred for this experiment
-    # So `shuf` the training dataset here.
+    # rather than DynamicBucketingSampler.
+    # Since negative audios(Aishell) tends to be longer than positive ones(HiMia).
+    # if DynamicBucketingSample is used, a batch may contain either all negative sample
+    # or positive sample.
+    # So `shuf` the training dataset here and use SingleCutSampler to load data.
     cat <(gunzip -c data/fbank/aishell_cuts_train.jsonl.gz) \
       <(gunzip -c data/fbank/cuts_train${train_set_channel}.jsonl.gz) | \
       grep -v _sp | \

egs/himia/wuw/run_ctc_tdnn.sh

@@ -26,6 +26,7 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
   log "Stage 0: Model training"
   python ./ctc_tdnn/train.py \
     --num-epochs $epoch \
+    --exp_dir $exp_dir
     --max-duration $max_duration
 fi
 
@@ -34,7 +35,7 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
   python ctc_tdnn/inference.py \
     --avg $avg \
     --epoch $epoch \
-    --exp-dir ${exp_dir}
+    --exp-dir $exp_dir
 fi
 
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then