Whisper-WebUI

A Gradio-based browser interface for 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!

Feature

• 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

Installation and Running

Prerequisite

To run this WebUI, you need to have git, python version 3.8 ~ 3.10, 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/ ( 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!

Automatic Installation

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. Git clone the repository

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

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

docker compose build 

3. Run the container

docker compose up

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

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

VRAM Usages

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.

Available models

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

Size	Parameters	English-only model	Multilingual model	Required VRAM	Relative speed
tiny	39 M	tiny.en	tiny	~1 GB	~32x
base	74 M	base.en	base	~1 GB	~16x
small	244 M	small.en	small	~2 GB	~6x
medium	769 M	medium.en	medium	~5 GB	~2x
large	1550 M	N/A	large	~10 GB	1x

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

TODO🗓

• 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 MVSEP-MDX23
• Add fast api script
• Support real-time transcription for microphone