https://github.com/jhj0517/whisper-webui
A Web UI for easy subtitle using whisper model.
https://github.com/jhj0517/whisper-webui
ai gradio open-source python pytorch web-ui whisper
Last synced: 12 days ago
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A Web UI for easy subtitle using whisper model.
- Host: GitHub
- URL: https://github.com/jhj0517/whisper-webui
- Owner: jhj0517
- License: apache-2.0
- Created: 2023-03-02T13:42:01.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2025-03-11T15:42:42.000Z (about 1 month ago)
- Last Synced: 2025-03-21T13:12:31.740Z (about 1 month ago)
- Topics: ai, gradio, open-source, python, pytorch, web-ui, whisper
- Language: Python
- Homepage:
- Size: 1.45 MB
- Stars: 1,812
- Watchers: 15
- Forks: 250
- Open Issues: 86
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# 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).
- Pre-processing audio input to separate BGM with [UVR](https://github.com/Anjok07/ultimatevocalremovergui).
- 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
### Pipeline Diagram
# Installation and Running
- ## Running with Pinokio
The app is able to run with [Pinokio](https://github.com/pinokiocomputer/pinokio).
1. Install [Pinokio Software](https://program.pinokio.computer/#/?id=install).
2. Open the software and search for Whisper-WebUI and install it.
3. Start the Whisper-WebUI and connect to the `http://localhost:7860`.
- ## 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`, `3.10 <= python <= 3.12`, `FFmpeg`.
**Edit `--extra-index-url` in the [`requirements.txt`](https://github.com/jhj0517/Whisper-WebUI/blob/master/requirements.txt) to match your device.
**
By default, the WebUI assumes you're using an Nvidia GPU and CUDA 12.4. If you're using Intel or another CUDA version, read the [`requirements.txt`](https://github.com/jhj0517/Whisper-WebUI/blob/master/requirements.txt) and edit `--extra-index-url`.
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/) **`3.10 ~ 3.12` is recommended.**
- 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. git clone this repository
```shell
git clone https://github.com/jhj0517/Whisper-WebUI.git
```
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)
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.
# 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.
Read [wiki](https://github.com/jhj0517/Whisper-WebUI/wiki/Command-Line-Arguments) for more info about CLI args.
If you want to use a fine-tuned model, manually place the models in `models/Whisper/` corresponding to the implementation.
Alternatively, if you enter the huggingface repo id (e.g, [deepdml/faster-whisper-large-v3-turbo-ct2](https://huggingface.co/deepdml/faster-whisper-large-v3-turbo-ct2)) in the "Model" dropdown, it will be automatically downloaded in the directory.
# REST API
If you're interested in deploying this app as a REST API, please check out [/backend](https://github.com/jhj0517/Whisper-WebUI/tree/master/backend).
## 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 )
- [x] Add background music separation pre-processing with [UVR](https://github.com/Anjok07/ultimatevocalremovergui)
- [x] Add fast api script
- [ ] Add CLI usages
- [ ] Support real-time transcription for microphone
### Translation 🌐
Any PRs that translate the language into [translation.yaml](https://github.com/jhj0517/Whisper-WebUI/blob/master/configs/translation.yaml) would be greatly appreciated!