Update README.md

README.md

@@ -3,82 +3,70 @@ sdk: gradio
 sdk_version: 5.16.0
 ---
 # Whisper-WebUI
-A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper).
-
-## Notebook
-If you wish to try this on Colab, you can do it in [here](https://colab.research.google.com/github/jhj0517/Whisper-WebUI/blob/master/notebook/whisper-webui.ipynb)!
+A Gradio-based browser interface for [Whisper](https://github.com/openai/whisper)
 
 # Features
-- Select the Whisper implementation you want to use between :
+- Select the Whisper implementation you want to use between:
    - [openai/whisper](https://github.com/openai/whisper)
    - [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper) (used by default)
    - [Vaibhavs10/insanely-fast-whisper](https://github.com/Vaibhavs10/insanely-fast-whisper)
-- Generate subtitles from various sources, including :
-  - Files
-  - Microphone
-- Currently supported output formats :
-  - csv 
-  - srt
-  - txt
-- Speech to Text Translation 
-  - From other languages to English. ( This is Whisper's end-to-end speech-to-text translation feature )
-  - Translate subtitle files using Facebook NLLB models
-- Pre-processing audio input with [Silero VAD](https://github.com/snakers4/silero-vad). 
-- Post-processing with speaker diarization using the [pyannote](https://huggingface.co/pyannote/speaker-diarization-3.1) model.
-   - To download the pyannote model, you need to have a Huggingface token and manually accept their terms in the pages below.
+- Generate transcriptions from various sources, including **files** & **microphone**
+- Currently supported output formats: **csv**, **srt** & **txt**
+- Speech to Text Translation:
+  - From other languages to English (This is Whisper's end-to-end speech-to-text translation feature)
+  - Translate transcription files using Facebook NLLB models
+- Pre-processing audio input with [Silero VAD](https://github.com/snakers4/silero-vad)
+- Post-processing with speaker diarization using the [pyannote](https://huggingface.co/pyannote/speaker-diarization-3.1) model:
+   - To download the pyannote model, you need to have a Huggingface token and manually accept their terms in the pages below:
       1. https://huggingface.co/pyannote/speaker-diarization-3.1
       2. https://huggingface.co/pyannote/segmentation-3.0
 
 # Installation and Running
-### 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 [`requirements.txt`](https://github.com/jhj0517/Whisper-WebUI/blob/master/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)
-- python : [https://www.python.org/downloads/](https://www.python.org/downloads/) **( If your python version is too new, torch will not install properly.)**
-- 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!**
-
-### Automatic Installation
-
-1. Download `Whisper-WebUI.zip` with the file corresponding to your OS from [v1.0.0](https://github.com/jhj0517/Whisper-WebUI/releases/tag/v1.0.0) and extract its contents. 
-2. Run `install.bat` or `install.sh` to install dependencies. (This will create a `venv` directory and install dependencies there.)
-3. Start WebUI with `start-webui.bat` or `start-webui.sh`
-4. To update the WebUI, run `update.bat` or `update.sh`
-
-And you can also run the project with command line arguments if you like to, see [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for a guide to arguments.
-
-- ## Running with Docker 
-
-1. Install and launch [Docker-Desktop](https://www.docker.com/products/docker-desktop/).
-
-2. Git clone the repository
 
-```sh
-git clone https://github.com/jhj0517/Whisper-WebUI.git
-```
-
-3. Build the image ( Image is about 7GB~ )
-
-```sh
-docker compose build 
-```
-
-4. Run the container 
-
-```sh
-docker compose up
-```
-
-5. Connect to the WebUI with your browser at `http://localhost:7860`
-
-If needed, update the [`docker-compose.yaml`](https://github.com/jhj0517/Whisper-WebUI/blob/master/docker-compose.yaml) to match your environment.
+- ## 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
+      
+      Please follow the links below to install the necessary software:
+      - git : [https://git-scm.com/downloads](https://git-scm.com/downloads)
+      - python : [https://www.python.org/downloads/](https://www.python.org/downloads/)
+      - 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!**
+
+    ### Installation Using the Script Files
+    
+      1. Download the the repository and extract its contents 
+      2. Run `install.bat` or `install.sh` to install dependencies (It will create a `venv` directory and install dependencies there)
+      3. Start WebUI with `start-webui.bat` or `start-webui.sh` (It will run `python app.py` after activating the venv)
+
+- ## Running with Docker
+
+    1. Install and launch [Docker-Desktop](https://www.docker.com/products/docker-desktop/)
+    
+    2. Get the repository
+    
+    3. Build the image ( Image is about 7GB~ )
+    
+    ```sh
+    docker compose build 
+    ```
+    
+    4. Run the container 
+    
+    ```sh
+    docker compose up
+    ```
+    
+    5. Connect to the WebUI with your browser at `http://localhost:7860`
+    
+    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
 
 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 |
@@ -86,12 +74,8 @@ According to faster-whisper, the efficiency of the optimized whisper model is as
 | openai/whisper    | fp16      | 5         | 4m30s | 11325MB         | 9439MB          |
 | faster-whisper    | fp16      | 5         | 54s   | 4755MB          | 3244MB          |
 
-If you want to use an implementation other than faster-whisper, use `--whisper_type` arg and the repository name.<br>
-Read [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for more info about CLI args.
-
 ## Available models
-This is Whisper's original VRAM usage table for 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      |
@@ -100,5 +84,4 @@ This is Whisper's original VRAM usage table for models.
 | medium |   769 M    |    `medium.en`     |      `medium`      |     ~5 GB     |      ~2x       |
 | large  |   1550 M   |        N/A         |      `large`       |    ~10 GB     |       1x       |
 
-
-`.en` models are for English only, and the cool thing is that you can use the `Translate to English` option from the "large" models!
+`.en` models are for English only, and you can use the `Translate to English` option from the other models