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.
And if you're not using an Nvida GPU, or using a different CUDA version than 12.4, edit the requirements.txt to match your environment.
Please follow the links below to install the necessary software:
- git : https://git-scm.com/downloads
- python : https://www.python.org/downloads/
3.10 ~ 3.12is 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.batorinstall.shto install dependencies. (It will create avenvdirectory and install dependencies there.) - Start WebUI with
start-webui.batorstart-webui.sh(It will runpython app.pyafter 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.
- Add DeepL API translation
- Add NLLB Model translation
- Integrate with faster-whisper
- Integrate with insanely-fast-whisper
- Integrate with whisperX ( Only speaker diarization part )
- Add background music separation pre-processing with UVR
- 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!
