Normalize wer computation

Closes #23 (closed)

dan/manager/metrics.py

@@ -31,6 +31,7 @@ class MetricManager:
         self.linked_metrics = {
             "cer": ["edit_chars", "nb_chars"],
             "wer": ["edit_words", "nb_words"],
+            "wer_no_punct": ["edit_words_no_punct", "nb_words_no_punct"],
             "loer": [
                 "edit_graph",
                 "nb_nodes_and_edges",
@@ -127,6 +128,14 @@ class MetricManager:
                 )
                 if output:
                     display_values["nb_words"] = np.sum(self.epoch_metrics["nb_words"])
+            elif metric_name == "wer_no_punct":
+                value = np.sum(self.epoch_metrics["edit_words_no_punct"]) / np.sum(
+                    self.epoch_metrics["nb_words_no_punct"]
+                )
+                if output:
+                    display_values["nb_words_no_punct"] = np.sum(
+                        self.epoch_metrics["nb_words_no_punct"]
+                    )
             elif metric_name in [
                 "loss",
                 "loss_ctc",
@@ -183,6 +192,20 @@ class MetricManager:
                     for (gt, pred) in zip(split_gt, split_pred)
                 ]
                 metrics["nb_words"] = [len(gt) for gt in split_gt]
+            elif metric_name == "wer_no_punct":
+                split_gt = [
+                    format_string_for_wer(gt, self.layout_tokens, remove_punct=True)
+                    for gt in values["str_y"]
+                ]
+                split_pred = [
+                    format_string_for_wer(pred, self.layout_tokens, remove_punct=True)
+                    for pred in values["str_x"]
+                ]
+                metrics["edit_words_no_punct"] = [
+                    edit_wer_from_formatted_split_text(gt, pred)
+                    for (gt, pred) in zip(split_gt, split_pred)
+                ]
+                metrics["nb_words_no_punct"] = [len(gt) for gt in split_gt]
             elif metric_name in [
                 "loss_ctc",
                 "loss_ce",
@@ -258,22 +281,23 @@ def nb_chars_cer_from_string(gt, layout_tokens=None):
     return len(format_string_for_cer(gt, layout_tokens))
 
 
-def edit_wer_from_string(gt, pred, layout_tokens=None):
+def edit_wer_from_string(gt, pred, layout_tokens=None, remove_punct=False):
     """
     Format and compute edit distance between two strings at word level
     """
-    split_gt = format_string_for_wer(gt, layout_tokens)
-    split_pred = format_string_for_wer(pred, layout_tokens)
+    split_gt = format_string_for_wer(gt, layout_tokens, remove_punct)
+    split_pred = format_string_for_wer(pred, layout_tokens, remove_punct)
     return edit_wer_from_formatted_split_text(split_gt, split_pred)
 
 
-def format_string_for_wer(str, layout_tokens):
+def format_string_for_wer(str, layout_tokens, remove_punct=False):
     """
     Format string for WER computation: remove layout tokens, treat punctuation as word, replace line break by space
     """
-    str = re.sub(
-        r"([\[\]{}/\\()\"'&+*=<>?.;:,!\-—_€#%°])", r" \1 ", str
-    )  # punctuation processed as word
+    if remove_punct:
+        str = re.sub(
+            r"([\[\]{}/\\()\"'&+*=<>?.;:,!\-—_€#%°])", "", str
+        )  # remove punctuation
     if layout_tokens is not None:
         str = keep_all_but_tokens(
             str, layout_tokens