refines

app.py

@@ -229,15 +229,15 @@ def get_wer_metrics(dataset):
     rows = []
     
     # First add row for number of examples
-    example_row = {"Metric": "Number of Examples"}
+    example_row = {"Methods": "Number of Examples"}
     for source in all_sources + ["OVERALL"]:
         example_row[source] = source_results[source]["Count"]
     rows.append(example_row)
     
-    # Then add rows for each WER method
-    no_lm_row = {"Metric": "Word Error Rate (No LM)"}
-    lm_ranking_row = {"Metric": "Word Error Rate (N-gram Ranking)"}
-    n_best_row = {"Metric": "Word Error Rate (Subwords Voting Correction)"}
+    # Then add rows for each WER method with simplified names
+    no_lm_row = {"Methods": "No LM"}
+    lm_ranking_row = {"Methods": "N-gram Ranking"}
+    n_best_row = {"Methods": "Subwords Voting"}
     
     for source in all_sources + ["OVERALL"]:
         no_lm_row[source] = source_results[source]["No LM Baseline"]
@@ -259,14 +259,14 @@ def format_dataframe(df):
     
     # Find the rows containing WER values
     wer_row_indices = []
-    for i, metric in enumerate(df["Metric"]):
-        if "WER" in metric or "Error Rate" in metric:
+    for i, method in enumerate(df["Methods"]):
+        if method not in ["Number of Examples"]:
             wer_row_indices.append(i)
     
     # Format WER values
     for idx in wer_row_indices:
         for col in df.columns:
-            if col != "Metric":
+            if col != "Methods":
                 value = df.loc[idx, col]
                 if pd.notna(value):
                     df.loc[idx, col] = f"{value:.4f}"
@@ -289,6 +289,9 @@ with gr.Blocks(title="ASR Text Correction Leaderboard") as demo:
     with gr.Row():
         refresh_btn = gr.Button("Refresh Leaderboard")
     
+    with gr.Row():
+        gr.Markdown("### Word Error Rate (WER)")
+    
     with gr.Row():
         try:
             initial_df = create_leaderboard()