fix the post normalization bug, avoid multiple words

egs/libriheavy/ASR/zipformer_prompt_asr/decode_baseline.py

@@ -57,7 +57,7 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
-from text_normalization import ref_text_normalization, remove_non_alphabetic
+from text_normalization import ref_text_normalization, remove_non_alphabetic, upper_only_alpha
 from train_baseline import (
     add_model_arguments,
     get_params,
@@ -742,10 +742,8 @@ def main():
                 new_ans = []
                 for item in results_dict[k]:
                     id, ref, hyp = item
-                    hyp = [remove_non_alphabetic(w.upper(), strict=False) for w in hyp]
-                    hyp = [w for w in hyp if w != ""]
-                    ref = [remove_non_alphabetic(w.upper(), strict=False) for w in ref]
-                    ref = [w for w in ref if w != ""]
+                    hyp = upper_only_alpha(" ".join(hyp)).split()
+                    ref = upper_only_alpha(" ".join(ref)).split()
                     new_ans.append((id, ref, hyp))
                 new_res[k] = new_ans 
             
@@ -754,6 +752,8 @@ def main():
                 test_set_name=test_set,
                 results_dict=new_res,
             )
+            
+            params.suffix = params.suffix.replace("-post-normalization", "")
 
     logging.info("Done!")
 

egs/libriheavy/ASR/zipformer_prompt_asr/decode_bert_with_style.py

@@ -62,7 +62,15 @@ from beam_search import (
 )
 from dataset import naive_triplet_text_sampling, random_shuffle_subset
 from utils import get_facebook_biasing_list
-from text_normalization import ref_text_normalization, remove_non_alphabetic, upper_only_alpha, upper_all_char, lower_all_char, lower_only_alpha
+from text_normalization import (
+    ref_text_normalization,
+    remove_non_alphabetic,
+    upper_only_alpha,
+    upper_all_char,
+    lower_all_char,
+    lower_only_alpha,
+    train_text_normalization,
+)
 from train_bert_encoder_with_style import (
     add_model_arguments,
     get_params,
@@ -313,6 +321,13 @@ def get_parser():
         help="If use a fixed context list for LibriSpeech decoding"
     )
     
+    parser.add_argument(
+        "--ls-distractors",
+        type=str2bool,
+        default=True,
+        help="If add distractors into context list for LibriSpeech decoding"
+    )
+    
     add_model_arguments(parser)
 
     return parser
@@ -393,20 +408,24 @@ def decode_one_batch(
     cuts = batch["supervisions"]["cut"]
     cut_ids = [c.supervisions[0].id for c in cuts]
     batch_size = feature.size(0)
-        
+    
     # get pre_text
     if "pre_text" in batch["supervisions"] and params.use_pre_text:
         pre_texts = batch["supervisions"]["pre_text"]
+        pre_texts = [train_text_normalization(t) for t in pre_texts]
     else:
         pre_texts = ["" for _ in range(batch_size)]
     
     if params.use_ls_context_list:
         pre_texts = [biasing_dict[id] for id in cut_ids]
+        if params.pre_text_transform == "mixed-punc":
+            pre_texts = [t.lower() for t in pre_texts]
         
     # get style_text
     if params.use_style_prompt:
         fixed_sentence = "Mixed-case English transcription, with punctuation. Actually, it is fully not related."
         style_texts = batch["supervisions"].get("style_text", [fixed_sentence for _ in range(batch_size)])
+        style_texts = [train_text_normalization(t) for t in style_texts]
     else:
         style_texts = ["" for _ in range(batch_size)] # use empty string
 
@@ -748,6 +767,8 @@ def main():
         
     if params.use_ls_context_list:
         params.suffix += f"-use-ls-context-list"
+        if params.ls_distractors:
+            params.suffix += f"-add-ls-context-distractors"
 
     if params.use_averaged_model:
         params.suffix += "-use-averaged-model"
@@ -886,9 +907,9 @@ def main():
 
     for test_set, test_dl in zip(test_sets, test_dl):
         if test_set == "ls-test-clean":
-            biasing_dict = get_facebook_biasing_list("test-clean")
+            biasing_dict = get_facebook_biasing_list("test-clean", use_distractors=params.ls_distractors)
         elif test_set == "ls-test-other":
-            biasing_dict = get_facebook_biasing_list("test-other")
+            biasing_dict = get_facebook_biasing_list("test-other", use_distractors=params.ls_distractors)
         else:
             biasing_dict = None
         
@@ -918,12 +939,10 @@ def main():
                 new_ans = []
                 for item in results_dict[k]:
                     id, ref, hyp = item
-                    hyp = [remove_non_alphabetic(w.upper(), strict=False) for w in hyp]
-                    hyp = [w for w in hyp if w != ""]
-                    ref = [remove_non_alphabetic(w.upper(), strict=False) for w in ref]
-                    ref = [w for w in ref if w != ""]
+                    hyp = upper_only_alpha(" ".join(hyp)).split()
+                    ref = upper_only_alpha(" ".join(ref)).split()                    
                     new_ans.append((id,ref,hyp))
-                new_res[k] = new_ans 
+                new_res[k] = new_ans
                 
             save_results(
                 params=params,