OpenAi Whisper Web Implementation

Gradio Web UI Implementation of Whisper open source model . Whisper is capable to translate and transcribe the human voice

Whisper OpenAi Tool Gradio Web implementation

Whisper is an automatic speech recognition (ASR) system Gradio Web UI Implementation

Installation

Install ffmeg on Your Device

# on Ubuntu or Debian
sudo apt update
sudo apt install ffmpeg

# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg

# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg

# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpeg

Download Program

mkdir whisper-sppech2txt
cd whisper-sppech2txt
git clone https://github.com/innovatorved/whisper-openai-gradio-implementation
.git .
pip install -r requirements.txt

Run Program

python app.py

Available models and languages

There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.

| 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 |

For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

Reference

• https://github.com/openai/whisper
• https://openai.com/blog/whisper/

Authors

• Ved Gupta

🚀 About Me

I'm a Developer i will feel the code then write .

Support

For support, email vedgupta@protonmail.com

🔗 Related Link: https://github.com/innovatorved/whisper-openai-gradio-implementation