minor fixes

egs/libriheavy/ASR/zipformer/decode.py

@@ -107,7 +107,7 @@ from beam_search import (
     modified_beam_search,
 )
 from lhotse.cut import Cut
-from text_normalization import remove_punc_to_upper,
+from text_normalization import remove_punc_to_upper
 from train import add_model_arguments, get_model, get_params
 
 from icefall.checkpoint import (
@@ -174,10 +174,7 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--exp-dir",
-        type=str,
-        default="zipformer/exp",
-        help="The experiment dir",
+        "--exp-dir", type=str, default="zipformer/exp", help="The experiment dir",
     )
 
     parser.add_argument(
@@ -285,7 +282,7 @@ def get_parser():
         type=str2bool,
         default=False,
         help="""Set to True, if the model was trained on texts with casing
-        and punctuation."""
+        and punctuation.""",
     )
 
     parser.add_argument(
@@ -352,9 +349,7 @@ def decode_one_batch(
         pad_len = 30
         feature_lens += pad_len
         feature = torch.nn.functional.pad(
-            feature,
-            pad=(0, 0, 0, pad_len),
-            value=LOG_EPS,
+            feature, pad=(0, 0, 0, pad_len), value=LOG_EPS,
         )
 
     encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
@@ -404,9 +399,7 @@ def decode_one_batch(
             hyps.append(hyp.split())
     elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_tokens = greedy_search_batch(
-            model=model,
-            encoder_out=encoder_out,
-            encoder_out_lens=encoder_out_lens,
+            model=model, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -434,9 +427,7 @@ def decode_one_batch(
                 )
             elif params.decoding_method == "beam_search":
                 hyp = beam_search(
-                    model=model,
-                    encoder_out=encoder_out_i,
-                    beam=params.beam_size,
+                    model=model, encoder_out=encoder_out_i, beam=params.beam_size,
                 )
             else:
                 raise ValueError(
@@ -505,9 +496,6 @@ def decode_dataset(
         warnings.simplefilter("ignore")
         for batch_idx, batch in enumerate(dl):
             texts = batch["supervisions"]["text"]
-            texts = [
-                simple_normalization(t) for t in texts
-            ]  # Do a simple normalization, as this is done during training
 
             cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
 
@@ -537,7 +525,6 @@ def decode_dataset(
 
                     results[f"{name}_norm"].extend(this_batch)
 
-
             num_cuts += len(texts)
 
             if batch_idx % log_interval == 0:
@@ -786,9 +773,7 @@ def main():
         )
 
         save_results(
-            params=params,
-            test_set_name=test_set,
-            results_dict=results_dict,
+            params=params, test_set_name=test_set, results_dict=results_dict,
         )
 
     logging.info("Done!")

egs/libriheavy/ASR/zipformer/text_normalization.py

@@ -43,8 +43,6 @@ def text_normalization(text: str) -> str:
 
 
 if __name__ == "__main__":
-
-    assert simple_cleanup("I like this 《book>") == "I like this <book>"
     assert remove_punc_to_upper("I like this 《book>") == "I LIKE THIS BOOK"
     assert (
         text_normalization("Hello Mrs st 21st world 3rd she 99th MR")