Update README.md

README.md

@@ -26,8 +26,8 @@ A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper
 - ## Run Locally
 
     ### Prerequisite
-      To run this WebUI, you need to have `git`, `python` version 3.8 ~ 3.10, `FFmpeg`<br>
-      And if you're not using an Nvida GPU, or using a different `CUDA` version than 12.4,  edit the **file requirements.txt** to match your environment
+    To run this WebUI, you need to have `git`, `python` version 3.8 ~ 3.10, `FFmpeg`<BR>
+    And if you're not using an Nvida GPU, or using a different `CUDA` version than 12.4,  edit the **file requirements.txt** to match your environment
       
       Please follow the links below to install the necessary software:
       - git : [https://git-scm.com/downloads](https://git-scm.com/downloads)
@@ -35,7 +35,7 @@ A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper
       - FFmpeg :  [https://ffmpeg.org/download.html](https://ffmpeg.org/download.html)
       - CUDA : [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads)
     
-      After installing FFmpeg, **make sure to add the `FFmpeg/bin` folder to your system PATH!**
+    After installing FFmpeg, make sure to **add** the `FFmpeg/bin` folder to your system **PATH**
 
     ### Installation Using the Script Files
     
@@ -63,25 +63,22 @@ A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper
     
     5. Connect to the WebUI with your browser at `http://localhost:7860`
     
-    If needed, update the **docker-compose.yaml** to match your environment
+    Note: If needed, update the **docker-compose.yaml** to match your environment
 
 # VRAM Usages
-This project is integrated with [faster-whisper](https://github.com/guillaumekln/faster-whisper) by default for better VRAM usage and transcription speed
+- This project is integrated with [faster-whisper](https://github.com/guillaumekln/faster-whisper) by default for better VRAM usage and transcription speed.<BR>According to faster-whisper, the efficiency of the optimized whisper model is as follows: 
+    | Implementation    | Precision | Beam size | Time  | Max. GPU memory | Max. CPU memory |
+    |-------------------|-----------|-----------|-------|-----------------|-----------------|
+    | openai/whisper    | fp16      | 5         | 4m30s | 11325MB         | 9439MB          |
+    | faster-whisper    | fp16      | 5         | 54s   | 4755MB          | 3244MB          |
 
-According to faster-whisper, the efficiency of the optimized whisper model is as follows: 
-| Implementation    | Precision | Beam size | Time  | Max. GPU memory | Max. CPU memory |
-|-------------------|-----------|-----------|-------|-----------------|-----------------|
-| openai/whisper    | fp16      | 5         | 4m30s | 11325MB         | 9439MB          |
-| faster-whisper    | fp16      | 5         | 54s   | 4755MB          | 3244MB          |
-
-## Available models
-This is Whisper's original VRAM usage table for models:
-|  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       |
+- Whisper's original VRAM usage table for available models:
+    |  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       |
 
 Note: `.en` models are for English only, and you can use the `Translate to English` option from the other models