openai-whisper

This is a sample webapp implementation of OpenAI Whisper, an automatic speech recognition (ASR) system, using Next.JS.

It records audio data automatically and uploads the audio data to the server for transcribing/translating then sends back the result to the front end. It is also possible to playback the recorded audio to verify the output.

Update: If you want to use Next 13 with experimental feature enabled (appDir), please check openai-whisper-api instead. Just set the flag to use whisper python module instead of whisper API.

---

• Using OpenAI Speech to Text API, please check openai-whisper-api

• If you are looking for voice-chat app using Whisper, please check openai-whisper-talk.

• For Nuxt.js version, please check openai-chatterbox.

Motivation

It has been said that Whisper itself is not designed to support real-time streaming tasks per se but it does not mean we cannot try, vain as it may be, lol.

So this project is my attempt to make an almost real-time transcriber web application using openai Whisper. The efficacy of which depends on how fast the server can transcribe/translate the audio.

I used Next.js so that I do not have to make separate backend and frontend apps.

As for the backend, I used exec to execute shell command invoking Whisper. I have not yet find a way to import it as a node.js module. All examples with import seem to be using python server.

import { exec } from 'child_process'

exec(`whisper './${filename}' --model tiny --language Japanese --task translate`, (err, stdout, stderr) => {
    if (err) {
        console.log(err)
    } else {
        console.log(stdout)
        console.log(stderr)
    }
})

Notice I am just using the tiny model to perform super fast transcribing task. This is all my system can handle otherwise it will come to a stand still.

The App

I changed the behavior of the app from previous version. Before, the app will record audio data continuously by some time interval, by default 5s. Right now, it will only start recording if it can detect sound.

There is a threshold setting to eliminate background noise from triggering the audio capture. By default it is set to -45dB (0dB is the loudest sound). Adjust the variable minDecibels in Settings if you want to set it to lower or higher depending on your needs.

In normal human conversation, it is said that we tend to pause, on average, around 2 seconds between each sentences. Keeping this in mind, if sound is not detected for more than 2 seconds, recording will stop and the audio data will be sent to the backend for transcribing. You can change this by editing the value of maxPause, by default set to 2500ms.

It is possible to play the uploaded audio and follow the text output since the time period is shown.

As for the code itself, I used class component (I know, I know...) because I had a difficult time to access state variables using hooks when I was developing.

Aside from minDecibels and maxPause, you can also change several Whisper options such as language, model and task from the Settings dialog. Please check Whisper's github repository for the explanation on the options.

There are still lots of things to do so this project is still a work in progress...

Setup

First, you need to install Whisper and its Python dependencies

$ pip install git+https://github.com/openai/whisper.git

You also need ffmpeg installed on your system

# macos
$ brew install ffmpeg

# windows using chocolatey
$ choco install ffmpeg

# windows using scoop
$ scoop install ffmpeg

By this time, you can test Whisper using command line

$ whisper myaudiofile.ogg --language Japanese --task translate

If that is successful, you can proceed to install this app.

Clone the repository and install the dependencies

$ git clone https://github.com/supershaneski/openai-whisper.git myproject

$ cd myproject

$ npm install

$ npm run dev

Open your browser to http://localhost:3006/ to load the application page.

Using HTTPS

You might want to run this app using https protocol. This is needed if you want to use a separate device for audio capture and use your machine as server.

In order to do so, prepare the proper certificate and key files and edit server.js at the root directory.

Then run

$ node server.js

Now, open your browser to https://localhost:3006/ to load the page.