Fix train.py

egs/libriheavy/ASR/zipformer/asr_datamodule.py

@@ -95,16 +95,6 @@ class LibriHeavyAsrDataModule:
             """,
         )
 
-        group.add_argument(
-            "--with-punctuation",
-            type=str2bool,
-            default=False,
-            help="""True to train the model on transcription with punctuation,
-            False to train the model on normalized transcription
-            (upper without punctuation).
-            """,
-        )
-
         group.add_argument(
             "--manifest-dir",
             type=Path,
@@ -413,35 +403,29 @@ class LibriHeavyAsrDataModule:
     @lru_cache()
     def train_small_cuts(self) -> CutSet:
         logging.info("About to get small subset cuts")
-        basename = f"libriheavy_{'punc_' if self.args.with_punctuation else ''}cuts_small.jsonl.gz"
+        return load_manifest_lazy(self.args.manifest_dir / "libriheavy_cuts_small.jsonl.gz")
-        return load_manifest_lazy(self.args.manifest_dir / basename)
 
     @lru_cache()
     def train_medium_cuts(self) -> CutSet:
         logging.info("About to get medium subset cuts")
-        basename = f"libriheavy_{'punc_' if self.args.with_punctuation else ''}cuts_medium.jsonl.gz"
+        return load_manifest_lazy(self.args.manifest_dir / "libriheavy_cuts_medium.jsonl.gz")
-        return load_manifest_lazy(self.args.manifest_dir / basename)
 
     @lru_cache()
     def train_large_cuts(self) -> CutSet:
         logging.info("About to get large subset cuts")
-        basename = f"libriheavy_{'punc_' if self.args.with_punctuation else ''}cuts_large.jsonl.gz"
+        return load_manifest_lazy(self.args.manifest_dir / "libriheavy_cuts_large.jsonl.gz")
-        return load_manifest_lazy(self.args.manifest_dir / basename)
 
     @lru_cache()
     def dev_cuts(self) -> CutSet:
         logging.info("About to get dev cuts")
-        basename = f"libriheavy_{'punc_' if self.args.with_punctuation else ''}cuts_dev.jsonl.gz"
+        return load_manifest_lazy(self.args.manifest_dir / "libriheavy_cuts_dev.jsonl.gz")
-        return load_manifest_lazy(self.args.manifest_dir / basename)
 
     @lru_cache()
     def test_clean_cuts(self) -> CutSet:
         logging.info("About to get the test-clean cuts")
-        basename = f"libriheavy_{'punc_' if self.args.with_punctuation else ''}cuts_test-clean.jsonl.gz"
+        return load_manifest_lazy(self.args.manifest_dir / "libriheavy_cuts_test_clean.jsonl.gz")
-        return load_manifest_lazy(self.args.manifest_dir / basename)
 
     @lru_cache()
     def test_other_cuts(self) -> CutSet:
         logging.info("About to get the test-other cuts")
-        basename = f"libriheavy_{'punc_' if self.args.with_punctuation else ''}cuts_test-other.jsonl.gz"
+        return load_manifest_lazy(self.args.manifest_dir / "libriheavy_cuts_test_other.jsonl.gz")
-        return load_manifest_lazy(self.args.manifest_dir / basename)

egs/libriheavy/ASR/zipformer/train.py

@@ -261,6 +261,7 @@ def add_model_arguments(parser: argparse.ArgumentParser):
     )
 
 
+
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -470,6 +471,13 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
+    parser.add_argument(
+        "--train-with-punctuation",
+        type=str2bool,
+        default=False,
+        help="If True, the training text will include casing and punctuation.",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -1178,9 +1186,20 @@ def run(rank, world_size, args):
     if params.inf_check:
         register_inf_check_hooks(model)
 
-    libriheavy = LibriHeavyAsrDataModule(args)
 
-    train_cuts = libriheavy.train_cuts()
+    def normalize_text(c: Cut):
+        text = c.supervisions[0].text
+        if params.train_with_punctuation:
+            table = str.maketrans("’‘，。；？！（）：-《》、“”【】", "'',.;?!(): <>/\"\"[]")
+            text = text.translate(table)
+        else:
+            text = text.replace("‘", "'")
+            text = text.replace("’", "'")
+            tokens = set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'")
+            text_list = [x.upper() if x in tokens else " " for x in text]
+            text = " ".join("".join(text_list).split()).strip()
+        c.supervisions[0].text = text
+        return c
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds
@@ -1204,7 +1223,7 @@ def run(rank, world_size, args):
         # In ./zipformer.py, the conv module uses the following expression
         # for subsampling
         T = ((c.num_frames - 7) // 2 + 1) // 2
-        tokens = sp.encode(c.supervisions[0].texts[0], out_type=str)
+        tokens = sp.encode(c.supervisions[0].text, out_type=str)
 
         if T < len(tokens):
             logging.warning(
@@ -1219,6 +1238,15 @@ def run(rank, world_size, args):
 
         return True
 
+    libriheavy = LibriHeavyAsrDataModule(args)
+
+    train_cuts = libriheavy.train_small_cuts()
+    if params.subset == 'M' or params.subset == 'L':
+        train_cuts += libriheavy.train_medium_cuts()
+    if params.subset == "L":
+        train_cuts += libriheavy.train_large_cuts()
+    train_cuts = train_cuts.map(normalize_text)
+
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
 
     if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
@@ -1232,15 +1260,8 @@ def run(rank, world_size, args):
         train_cuts, sampler_state_dict=sampler_state_dict
     )
 
-    def add_texts(c: Cut):
+    valid_cuts = libriheavy.dev_cuts()
-        text = c.supervisions[0].text
+    valid_cuts = valid_cuts.map(normalize_text)
-        c.supervisions[0].texts = [text]
-        return c
-
-    valid_cuts = libriheavy.librispeech_dev_clean_cuts()
-    valid_cuts += libriheavy.librispeech_dev_other_cuts()
-
-    valid_cuts = valid_cuts.map(add_texts)
 
     valid_dl = libriheavy.valid_dataloaders(valid_cuts)