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

• 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

Installation and Running

• On Windows OS

Prerequisite

To run this WebUI, you need to have git, python version 3.8 ~ 3.10, CUDA version above 12.0 and FFmpeg.

Please follow the links below to install the necessary software:

• CUDA : https://developer.nvidia.com/cuda-downloads
• 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

After installing FFmpeg, make sure to add the FFmpeg/bin folder to your system PATH!

Automatic Installation

If you have satisfied the prerequisites listed above, you are now ready to start Whisper-WebUI.

1. Run Install.bat from Windows Explorer as a regular, non-administrator user.
2. After installation, run the start-webui.bat.
3. Open your web browser and go to http://localhost:7860

( If you're running another Web-UI, it will be hosted on a different port , such as localhost:7861, localhost:7862, and so on )

And you can also run the project with command line arguments if you like by running user-start-webui.bat, see wiki for a guide to arguments.

• Docker ( On Other OS )

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

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 the original Open AI whisper implementation instead of optimized whisper, you can set the command line argument --disable_faster_whisper to True. See the wiki for more information.

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!