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| # Whisper-WebUI | |
| A Gradio-based browser interface for [Whisper](https://github.com/openai/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 | |
| - 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 | |
| - Youtube | |
| - Microphone | |
| - Currently supported subtitle formats : | |
| - SRT | |
| - WebVTT | |
| - txt ( only text file without timeline ) | |
| - 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 | |
| - Translate subtitle files using DeepL API | |
| - 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` and `CUDA` (if you use NVIDIA GPU) version above 12.0 | |
| 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. Build the image | |
| ```sh | |
| docker build -t whisper-webui:latest . | |
| ``` | |
| 2. Run the container with commands | |
| - For bash : | |
| ```sh | |
| docker run --gpus all -d \ | |
| -v /path/to/models:/Whisper-WebUI/models \ | |
| -v /path/to/outputs:/Whisper-WebUI/outputs \ | |
| -p 7860:7860 \ | |
| -it \ | |
| whisper-webui:latest --server_name 0.0.0.0 --server_port 7860 | |
| ``` | |
| - For PowerShell: | |
| ```shell | |
| docker run --gpus all -d ` | |
| -v /path/to/models:/Whisper-WebUI/models ` | |
| -v /path/to/outputs:/Whisper-WebUI/outputs ` | |
| -p 7860:7860 ` | |
| -it ` | |
| whisper-webui:latest --server_name 0.0.0.0 --server_port 7860 | |
| ``` | |
| # 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 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. | |
| | 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! | |
| ## TODO🗓 | |
| - [x] Add DeepL API translation | |
| - [x] Add NLLB Model translation | |
| - [x] Integrate with faster-whisper | |
| - [x] Integrate with insanely-fast-whisper | |
| - [x] Integrate with whisperX ( Only speaker diarization part ) | |
| - [ ] Add fast api script | |
| - [ ] Support real-time transcription for microphone | |