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.
    1. https://huggingface.co/pyannote/speaker-diarization-3.1
    2. https://huggingface.co/pyannote/segmentation-3.0

• Running with Pinokio

The app is able to run with Pinokio.

1. Install Pinokio Software.
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.

2. Git clone the repository

git clone https://github.com/jhj0517/Whisper-WebUI.git

3. Build the image ( Image is about 7GB~ )

docker compose build 

4. Run the container

docker compose up

5. Connect to the WebUI with your browser at http://localhost:7860

If needed, update the docker-compose.yaml to match your environment.

• Run Locally

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 ~ 3.12 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!

1. git clone this repository

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 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:

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.

This is Whisper's original VRAM usage table for models.

.en models are for English only, and the cool thing is that you can use the Translate to English option from the "large" models!

• 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
• Support real-time transcription for microphone

Any PRs that translate the language into translation.yaml would be greatly appreciated!