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Update README

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@@ -41,6 +41,31 @@ The app is able to run with [Pinokio](https://github.com/pinokiocomputer/pinokio
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  2. Open the software and search for Whisper-WebUI and install it.
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  3. Start the Whisper-WebUI and connect to the `http://localhost:7860`.
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  - ## Running with Docker
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  1. Install and launch [Docker-Desktop](https://www.docker.com/products/docker-desktop/).
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  If needed, update the [`docker-compose.yaml`](https://github.com/jhj0517/Whisper-WebUI/blob/master/docker-compose.yaml) to match your environment.
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- - ## Run Locally
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-
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- ### Prerequisite
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- To run this WebUI, you need to have `git`, `python` version 3.8 ~ 3.10, `FFmpeg`. <br>
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- 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.
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-
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- Please follow the links below to install the necessary software:
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- - git : [https://git-scm.com/downloads](https://git-scm.com/downloads)
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- - python : [https://www.python.org/downloads/](https://www.python.org/downloads/) **( If your python version is too new, torch will not install properly.)**
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- - FFmpeg : [https://ffmpeg.org/download.html](https://ffmpeg.org/download.html)
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- - CUDA : [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads)
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-
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- After installing FFmpeg, **make sure to add the `FFmpeg/bin` folder to your system PATH!**
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-
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- ### Automatic Installation
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-
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- 1. git clone this repository
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- ```shell
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- https://github.com/jhj0517/Whisper-WebUI.git
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- ```
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- 2. Run `install.bat` or `install.sh` to install dependencies. (This will create a `venv` directory and install dependencies there.)
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- 3. Start WebUI with `start-webui.bat` or `start-webui.sh`
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-
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- 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.
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-
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  # VRAM Usages
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  This project is integrated with [faster-whisper](https://github.com/guillaumekln/faster-whisper) by default for better VRAM usage and transcription speed.
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  2. Open the software and search for Whisper-WebUI and install it.
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  3. Start the Whisper-WebUI and connect to the `http://localhost:7860`.
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+ - ## Run Locally
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+
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+ ### Prerequisite
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+ To run this WebUI, you need to have `git`, `python` version 3.8 ~ 3.10, `FFmpeg`. <br>
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+ 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.
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+
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+ Please follow the links below to install the necessary software:
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+ - git : [https://git-scm.com/downloads](https://git-scm.com/downloads)
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+ - python : [https://www.python.org/downloads/](https://www.python.org/downloads/) **( If your python version is too new, torch will not install properly.)**
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+ - FFmpeg : [https://ffmpeg.org/download.html](https://ffmpeg.org/download.html)
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+ - CUDA : [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads)
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+
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+ After installing FFmpeg, **make sure to add the `FFmpeg/bin` folder to your system PATH!**
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+
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+ ### Automatic Installation
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+
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+ 1. git clone this repository
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+ ```shell
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+ https://github.com/jhj0517/Whisper-WebUI.git
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+ ```
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+ 2. Run `install.bat` or `install.sh` to install dependencies. (This will create a `venv` directory and install dependencies there.)
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+ 3. Start WebUI with `start-webui.bat` or `start-webui.sh`
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+
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+ 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.
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+
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  - ## Running with Docker
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  1. Install and launch [Docker-Desktop](https://www.docker.com/products/docker-desktop/).
 
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  If needed, update the [`docker-compose.yaml`](https://github.com/jhj0517/Whisper-WebUI/blob/master/docker-compose.yaml) to match your environment.
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  # VRAM Usages
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  This project is integrated with [faster-whisper](https://github.com/guillaumekln/faster-whisper) by default for better VRAM usage and transcription speed.
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