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.
• 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

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
• python : https://www.python.org/downloads/ ( If your python version is too new, torch will not install properly.)
• 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. Download Whisper-WebUI.zip with the file corresponding to your OS from 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 for a guide to arguments.

• Running with Docker

1. Build the image

docker build -t whisper-webui:latest . 

2. Run the container with commands

• For bash :

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:

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

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 fast api script