up

app.py

@@ -70,7 +70,6 @@ def parse_metrics_rows(meta):
         dataset = result["dataset"]["type"]
         if "args" not in result["dataset"]:
             continue
-        dataset_config = result["dataset"]["args"]
         row = {"dataset": dataset, "lang": lang}
         for metric in result["metrics"]:
             type = metric["type"].lower().strip()
@@ -117,8 +116,8 @@ def sort_datasets(datasets):
 
 @st.cache(ttl=600)
 def generate_dataset_info(datasets):
-    msg = f"""
-    The models have been trained and/or evaluated on the following datasets: 
+    msg = """
+    The models have been trained and/or evaluated on the following datasets:
     """
     for dataset_id in datasets:
         if dataset_id in suggested_datasets:
@@ -141,7 +140,7 @@ st.sidebar.image("logo.png", width=200)
 st.markdown("# The 🤗 Speech Bench")
 
 st.markdown(
-    "This is a leaderboard over all speech recognition models and datasets.\n\n"
+    f"This is a leaderboard of **{len(dataframe)}** speech recognition models and **{len(dataframe['dataset'].unique())}** datasets.\n\n"
     "⬅ Please select the language you want to find a model for from the dropdown on the left."
 )
 
@@ -216,4 +215,4 @@ else:
 
 st.markdown(
     "Want to beat the Leaderboard? Don't see your speech recognition model show up here? Simply add the `hf-asr-leaderboard` tag to your model card alongside your evaluation metrics. See [this commit](https://huggingface.co/facebook/wav2vec2-base-960h/commit/88338305603a4d8db25aca96e669beb5f7dc65cb) as an example."
-)
+)