
Whisper-WebUI
A Web UI for easy subtitle using whisper model.
Stars: 1772

Whisper-WebUI is a Gradio-based browser interface for Whisper, serving as an Easy Subtitle Generator. It supports generating subtitles from various sources such as files, YouTube, and microphone. The tool also offers speech-to-text and text-to-text translation features, utilizing Facebook NLLB models and DeepL API. Users can translate subtitle files from other languages to English and vice versa. The project integrates faster-whisper for improved VRAM usage and transcription speed, providing efficiency metrics for optimized whisper models. Additionally, users can choose from different Whisper models based on size and language requirements.
README:
A Gradio-based browser interface for Whisper. You can use it as an Easy Subtitle Generator!
If you wish to try this on Colab, you can do it in here!
- Select the Whisper implementation you want to use between :
- openai/whisper
- SYSTRAN/faster-whisper (used by default)
- 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.
- Pre-processing audio input to separate BGM with UVR.
- Post-processing with speaker diarization using the pyannote model.
- To download the pyannote model, you need to have a Huggingface token and manually accept their terms in the pages below.
The app is able to run with Pinokio.
- Install Pinokio Software.
- Open the software and search for Whisper-WebUI and install it.
- Start the Whisper-WebUI and connect to the
http://localhost:7860
.
-
Install and launch Docker-Desktop.
-
Git clone the repository
git clone https://github.com/jhj0517/Whisper-WebUI.git
- Build the image ( Image is about 7GB~ )
docker compose build
- Run the container
docker compose up
- Connect to the WebUI with your browser at
http://localhost:7860
If needed, update the docker-compose.yaml
to match your environment.
To run this WebUI, you need to have git
, 3.10 <= python <= 3.12
, FFmpeg
.
Edit --extra-index-url
in the 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
and edit --extra-index-url
.
Please follow the links below to install the necessary software:
- git : https://git-scm.com/downloads
- python : https://www.python.org/downloads/
3.10 ~ 3.12
is recommended. - FFmpeg : https://ffmpeg.org/download.html
- CUDA : https://developer.nvidia.com/cuda-downloads
After installing FFmpeg, make sure to add the FFmpeg/bin
folder to your system PATH!
- git clone this repository
git clone https://github.com/jhj0517/Whisper-WebUI.git
- Run
install.bat
orinstall.sh
to install dependencies. (It will create avenv
directory and install dependencies there.) - Start WebUI with
start-webui.bat
orstart-webui.sh
(It will runpython app.py
after activating the venv)
And you can also run the project with command line arguments if you like to, see wiki for a guide to arguments.
This project is integrated with 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 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) in the "Model" dropdown, it will be automatically downloaded in the directory.
If you're interested in deploying this app as a REST API, please check out /backend.
- [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
- [x] Add fast api script
- [ ] Add CLI usages
- [ ] Support real-time transcription for microphone
Any PRs that translate the language into translation.yaml would be greatly appreciated!
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