README.md

metadata

sdk: gradio
sdk_version: 5.16.0

Whisper-WebUI

A Gradio-based browser interface for Whisper

Features

• Select the Whisper implementation you want to use between:
  • openai/whisper
  • SYSTRAN/faster-whisper (used by default)
  • Vaibhavs10/insanely-fast-whisper
• Generate transcriptions from various sources, including files & microphone
• Currently supported output formats: csv, srt & txt
• Speech to Text Translation:
  • From other languages to English (This is Whisper's end-to-end speech-to-text translation feature)
  • Translate transcription files using Facebook NLLB models
• 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

• Run Locally

  Prerequisite

  To run this WebUI, you need to have git, python version 3.8 ~ 3.10 & FFmpeg.
  If you're not using an Nvida GPU, or using a different CUDA version than 12.4, edit the file 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/
  • 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

  Installation using the script files

  1. Download the the repository and extract its contents
  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)

• Running with Docker

  1. Install and launch Docker-Desktop

  2. Get the repository

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

  4. Docker commands:

     Build the image ( Image is about ~7GB)

     docker compose build 
     

     Run the container

     docker compose up
     

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

• Whisper's original VRAM usage table for available 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

  Note: .en models are for English only, and you can use the Translate to English option from the other models