update title

app.py

@@ -381,6 +381,24 @@ with demo:
             ##### 3. Menerapkan pengelompokan aglomeratif pada pengkodean untuk mengidentifikasi pembicara untuk setiap segmen.
             ''')
             
+        with gr.Row():
+            gr.Markdown('''
+            ## Available models and languages
+
+            There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model; actual speed may vary depending on many factors including the available hardware.
+
+            |  Size  | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
+            |:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:|
+            |  tiny  |    39 M    |     `tiny.en`      |       `tiny`       |     ~1 GB     |      ~32x      |
+            |  base  |    74 M    |     `base.en`      |       `base`       |     ~1 GB     |      ~16x      |
+            | small  |   244 M    |     `small.en`     |      `small`       |     ~2 GB     |      ~6x       |
+            | medium |   769 M    |    `medium.en`     |      `medium`      |     ~5 GB     |      ~2x       |
+            | large  |   1550 M   |        N/A         |      `large`       |    ~10 GB     |       1x       |
+
+            The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
+            ''')
+         
+            
         # with gr.Row():         
         #     gr.Markdown('''
         #         ### You can test by following examples: