Update app.py

app.py

@@ -291,21 +291,21 @@ def create_interface():
         #         """)
         
         # gr.Markdown("---")
-        gr.Markdown("""
-                    ## 🔑 Key Findings
+        # gr.Markdown("""
+        #             ## 🔑 Key Findings
                     
-                    1. **Outstanding Speech Quality**  
-                       Several models—namely **Kokoro-82M**, **csm-1b**, **Spark-TTS-0.5B**, **Orpheus-3b-0.1-ft**, **F5-TTS**, and **Llasa-3B**—delivered exceptionally natural, clear, and realistic synthesized speech. Among these, **csm-1b** and **F5-TTS** stood out as the most well-rounded: they combined top-tier naturalness and intelligibility with solid controllability.
+        #             1. **Outstanding Speech Quality**  
+        #                Several models—namely **Kokoro-82M**, **csm-1b**, **Spark-TTS-0.5B**, **Orpheus-3b-0.1-ft**, **F5-TTS**, and **Llasa-3B**—delivered exceptionally natural, clear, and realistic synthesized speech. Among these, **csm-1b** and **F5-TTS** stood out as the most well-rounded: they combined top-tier naturalness and intelligibility with solid controllability.
                     
-                    2. **Superior Controllability**  
-                       **Zonos-v0.1-transformer** emerged as the leader in fine-grained control: it offers detailed adjustments for prosody, emotion, and audio quality, making it ideal for use cases that demand precise voice modulation.
+        #             2. **Superior Controllability**  
+        #                **Zonos-v0.1-transformer** emerged as the leader in fine-grained control: it offers detailed adjustments for prosody, emotion, and audio quality, making it ideal for use cases that demand precise voice modulation.
                     
-                    3. **Performance vs. Footprint Trade-off**  
-                       Smaller models (e.g., **Kokoro-82M** at 82 million parameters) can still achieve “Good” or “Excellent” ratings in many scenarios, especially when efficient inference or low VRAM usage is critical. Larger models (1 billion–3 billion+ parameters) generally offer more versatility—handling multilingual synthesis, zero-shot voice cloning, and multi-speaker generation—but require heavier compute resources.
+        #             3. **Performance vs. Footprint Trade-off**  
+        #                Smaller models (e.g., **Kokoro-82M** at 82 million parameters) can still achieve “Good” or “Excellent” ratings in many scenarios, especially when efficient inference or low VRAM usage is critical. Larger models (1 billion–3 billion+ parameters) generally offer more versatility—handling multilingual synthesis, zero-shot voice cloning, and multi-speaker generation—but require heavier compute resources.
                     
-                    4. **Special Notes on Multilingual & Cloning Capabilities**  
-                       **Spark-TTS-0.5B** and **XTTS-v2** excel at cross-lingual and zero-shot voice cloning, making them strong candidates for projects that need multi-language support or short-clip cloning. **Llama-OuteTTS-1.0-1B** and **MegaTTS3** also offer multilingual input handling, though they may require careful sampling parameter tuning to achieve optimal results.  
-                            """)
+        #             4. **Special Notes on Multilingual & Cloning Capabilities**  
+        #                **Spark-TTS-0.5B** and **XTTS-v2** excel at cross-lingual and zero-shot voice cloning, making them strong candidates for projects that need multi-language support or short-clip cloning. **Llama-OuteTTS-1.0-1B** and **MegaTTS3** also offer multilingual input handling, though they may require careful sampling parameter tuning to achieve optimal results.  
+        #                     """)
         
         # Search and Filter Section
         with gr.Row():