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| # Whisper-WebUI | |
| A Gradio-based browser interface for Whisper. You can use it as an Easy Subtitle Generator! | |
|  | |
| ## 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)! | |
| # Feature | |
| - Generate subtitles from various sources, including : | |
| - Files | |
| - Youtube | |
| - Microphone | |
| - Currently supported subtitle formats : | |
| - SRT | |
| - WebVTT | |
| - Speech to Text Translation | |
| - From other languages to English. ( This is Whisper's end-to-end speech-to-text translation feature ) | |
| - Text to Text Translation | |
| - Translate subtitle files using Facebook NLLB models | |
| # Installation and Running | |
| ## Prerequisite | |
| To run Whisper, you need to have `git`, `python` version 3.8 ~ 3.10 and `FFmpeg`. | |
| 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) | |
| After installing FFmpeg, **make sure to add the `FFmpeg/bin` folder to your system PATH!** | |
| ## Automatic Installation | |
| If you have satisfied the prerequisites listed above, you are now ready to start Whisper-WebUI. | |
| 1. Run `Install.bat` from Windows Explorer as a regular, non-administrator user. | |
| 2. After installation, run the `start-webui.bat`. (It will automatically download the model if it is not already installed.) | |
| 3. Open your web browser and go to `http://localhost:7860` | |
| ( If you're running another Web-UI, it will be hosted on a different port , such as `localhost:7861`, `localhost:7862`, and so on ) | |
| And you can also run the project with command line arguments if you like by running `user-start-webui.bat`, see [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for a guide to arguments. | |
| # VRAM Usages | |
| 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 | | |
| |-------------------|-----------|-----------|-------|-----------------|-----------------| | |
| | openai/whisper | fp16 | 5 | 4m30s | 11325MB | 9439MB | | |
| | faster-whisper | fp16 | 5 | 54s | 4755MB | 3244MB | | |
| If you want to use the original Open AI whisper implementation instead of optimized whisper, you can set the command line argument `DISABLE_FASTER_WHISPER` to `True`. See the [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for more information. | |
| ## 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 | | |
| `.en` models are for English only, and the cool thing is that you can use the `Translate to English` option from the "large" models! | |