Update tabs descriptions

app.py

@@ -9,13 +9,14 @@ def restart_space():
     API.restart_space(repo_id=REPO_ID)
 
 
-demo = gr.Blocks(css=custom_css)
-with demo:
+app = gr.Blocks(css=custom_css)
+
+with app:
     # Init class Leaderboard
     leaderboard = Leaderboard()
 
     # Custom CSS styling for tab-item components
-    demo.css = """
+    app.css = """
     .tab-item {
         font-size: 10px;
         padding: 10px 20px;
@@ -27,14 +28,13 @@ with demo:
     <style>
         @import url('https://fonts.googleapis.com/css2?family=Ubuntu:wght@400;700&display=swap');
     </style>
-    <h1 style="text-align:center; font-family: 'Ubuntu', sans-serif; font-size: 48px; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);">
+    <h1 style="text-align: center; font-family: 'Ubuntu', sans-serif; font-size: 36px; color: #002d69;">
         Open Voice Cloning Leaderboard
     </h1>
-    <p style="text-align:center; font-size: 17px; width: 85%; margin: 0 auto;">
-        Welcome to the <strong>Open Voice Cloning Leaderboard</strong> – an innovative platform for ranking voice cloning models, 
-        offering precise evaluations of their performance. This leaderboard stands out for its detailed analysis of key audio features, 
-        allowing users to not only assess the overall quality of a model but also gain a deeper understanding of how specific aspects of sound 
-        influence the final outcome.
+    <p style="text-align:center; font-size: 15px; width: 85%; margin: 0 auto;">
+        The <b>Open Voice Cloning Leaderboard</b> ranks and evaluates the voice cloning models across 
+        diverse datasets, including emotional speech.<br>It also delivers an in-depth analysis of how 
+        different acoustic features shape the final results.
     </p>
     """)
 
@@ -54,15 +54,8 @@ with demo:
             '''
             with gr.TabItem("Overall", elem_id="Overall", id=1, elem_classes="tab-item"):
                 gr.Markdown(value="""
-                ### Overall Leaderboard
-
-                <div style="text-align: center;">
-                    This 'Overall' leaderboard presents WavLM metric values across all datasets. 
-                    It includes an 'Average' column for the overall score and 
-                    dedicated columns displaying WavLM values for each dataset individually. 
-                    Additionally, the leaderboard includes the LibriSpeech Test Clean dataset, 
-                    which was used to evaluate the quality of generative models like Voicebox.
-                </div>
+                The results represent the cosine similarity between the speaker embeddings 
+                of the original and cloned samples, generated by the WavLM model.
                 """)
                 # Create and display leaderboard table
                 leaderboard_dataframe = leaderboard.create_leaderboard_data('All', 'wavlm', 'emotion')
@@ -78,15 +71,9 @@ with demo:
             '''
             with gr.TabItem("Emotions", elem_id="Emotions", id=2, elem_classes="tab-item"):
                 gr.Markdown(value="""
-                ### Emotions Leadreboard
-
-                <div style="text-align: center;">
-                    This 'Emotions' leaderboard provides WavLM metric values with filtering options for both 'Emotion' and 'Dataset'. 
-                    Selecting 'Emotion' generates a table with columns representing datasets, 
-                    each showing the WavLM score for the chosen emotion. Alternatively, 
-                    selecting 'Dataset' creates a table with columns representing emotions, 
-                    displaying the WavLM score for each within the selected dataset.
-                </div>
+                The results represent the cosine similarity between the speaker embeddings 
+                of the original and cloned samples, generated by the WavLM model. The values 
+                can be filtered by dataset or emotional state.
                 """)
 
                 # UI for selecting dataset and emotion options
@@ -129,16 +116,9 @@ with demo:
             '''
             with gr.TabItem("Features", elem_id="Features", id=3, elem_classes="tab-item"):
                 gr.Markdown(value="""
-                ### Features Leadreboard
-
-                <div style="text-align: center;">
-                    This 'Features' leaderboard provides filtering options similar to the 'Emotions' tab, 
-                    with additional flexibility through a 'Feature' filter. Users can select 'Emotion' to generate a table 
-                    with columns representing datasets, each displaying scores for the chosen emotion, 
-                    or 'Dataset' to view columns for emotions within the selected dataset. 
-                    The 'Feature' filter lets users select a specific feature for display, 
-                    focusing this leaderboard on metrics other than WavLM
-                </div>
+                The results represent the cosine similarity between the values of selected 
+                acoustic features of the original and cloned samples. The values 
+                can be filtered by dataset or emotional state.
                 """)
 
                 # UI for selecting dataset, emotion, and feature options
@@ -233,4 +213,4 @@ with demo:
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=1800)
 scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+app.queue(default_concurrency_limit=40).launch()