update comments

egs/himia/wuw/ctc_tdnn/asr_datamodule.py

@@ -197,7 +197,7 @@ class HiMiaWuwDataModule:
             default="_7_01",
             help="""channel of HI_MIA train dataset.
             All channels are used if it is set "all".
-            Please refer to state 6 in prepare.sh for its meaning and other
+            Please refer to stage 6 in prepare.sh for its meaning and other
             potential values. Currently, Only "_7_01" is verified.
             """,
         )
@@ -207,7 +207,7 @@ class HiMiaWuwDataModule:
             default="_7_01",
             help="""channel of HI_MIA dev dataset.
             All channels are used if it is set "all".
-            Please refer to state 6 in prepare.sh for its meaning and other
+            Please refer to stage 6 in prepare.sh for its meaning and other
             potential values. Currently, Only "_7_01" is verified.
             """,
         )

egs/himia/wuw/ctc_tdnn/graph.py

@@ -19,7 +19,7 @@
 from typing import List
 
 
-def ctc_trivial_decoding_graph(wakeup_word_tokens: List[int]):
+def ctc_trivial_decoding_graph(wakeup_word_tokens: List[int]) -> str:
     """
     A graph starts with blank/unknown and following by wakeup word.
 
@@ -27,6 +27,10 @@ def ctc_trivial_decoding_graph(wakeup_word_tokens: List[int]):
       wakeup_word_tokens: A sequence of token ids corresponding wakeup_word.
       It should not contain 0 and 1.
       We assume 0 is for blank and 1 is for unknown.
+    Returns:
+      Returns a finite-state transducer in string format,
+      used as a decoding graph.
+      Arcs are separated with "\n".
     """
     assert 0 not in wakeup_word_tokens
     assert 1 not in wakeup_word_tokens

egs/himia/wuw/ctc_tdnn/tokenizer.py

@@ -23,15 +23,15 @@ from typing import List, Tuple
 class WakeupWordTokenizer(object):
     def __init__(
         self,
-        wakeup_word: str = "",
+        wakeup_word: str,
-        wakeup_word_tokens: List[int] = None,
+        wakeup_word_tokens: List[int],
     ) -> None:
         """
         Args:
           wakeup_word: content of positive samples.
-            A sample will be treated as a negative sample unless its context
+            A sample will be treated as a negative sample unless its content
             is exactly the same to key_words.
-          wakeup_word_tokens: A list if int represents token ids of wakeup_word.
+          wakeup_word_tokens: A list of int representing token ids of wakeup_word.
             For example: the pronunciation of "你好米雅" is
             "n i h ao m i y a".
             Suppose we are using following lexicon:
@@ -67,7 +67,7 @@ class WakeupWordTokenizer(object):
     def texts_to_token_ids(
         self, texts: List[str]
     ) -> Tuple[torch.Tensor, torch.Tensor, int]:
-        """Convert a list of texts to a list of k2.Fsa based texts.
+        """Convert a list of texts to parameters needed by CTC loss.
 
         Args:
           texts:
@@ -76,7 +76,7 @@ class WakeupWordTokenizer(object):
         Returns:
           Return a tuple of 3 elements.
           The first one is torch.Tensor(List[List[int]]),
-          each List[int] is tokens sequence for each a reference text.
+          each List[int] is tokens sequence for each reference text.
 
           The second one is number of tokens for each sample,
           mainly used by CTC loss.
@@ -89,13 +89,13 @@ class WakeupWordTokenizer(object):
         number_positive_samples = 0
         for utt_text in texts:
             if utt_text == self.wakeup_word:
-                batch_token_ids.append(self.wakeup_word_tokens)
+                batch_token_ids.extend(self.wakeup_word_tokens)
                 target_lengths.append(self.positive_number_tokens)
                 number_positive_samples += 1
             else:
-                batch_token_ids.append(self.negative_word_tokens)
+                batch_token_ids.extend(self.negative_word_tokens)
                 target_lengths.append(self.negative_number_tokens)
 
-        target = torch.tensor(list(itertools.chain.from_iterable(batch_token_ids)))
+        target = torch.tensor(batch_token_ids)
         target_lengths = torch.tensor(target_lengths)
         return target, target_lengths, number_positive_samples

egs/himia/wuw/ctc_tdnn/train.py

@@ -531,6 +531,9 @@ def run(rank, world_size, args):
 
     model = Tdnn(params.feature_dim, params.num_class)
 
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
     checkpoints = load_checkpoint_if_available(params=params, model=model)
 
     model.to(device)