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useWhisper

React Hook for OpenAI Whisper API with speech recorder, real-time transcription and silence removal built-in

  • Demo

  • Real-Time transcription demo
use-whisper-real-time-transcription.mp4

  • Announcement

    useWhisper for React Native is being developed.

Repository: https://github.com/chengsokdara/use-whisper-native

Progress: chengsokdara/use-whisper-native#1

  • Install

npm i @chengsokdara/use-whisper

yarn add @chengsokdara/use-whisper

  • Usage

import { useWhisper } from '@chengsokdara/use-whisper'

const App = () => {
  const {
    recording,
    speaking,
    transcribing,
    transcript,
    pauseRecording,
    startRecording,
    stopRecording,
  } = useWhisper({
    apiKey: process.env.OPENAI_API_TOKEN, // YOUR_OPEN_AI_TOKEN
  })

  return (
    <div>
      <p>Recording: {recording}</p>
      <p>Speaking: {speaking}</p>
      <p>Transcribing: {transcribing}</p>
      <p>Transcribed Text: {transcript.text}</p>
      <button onClick={() => startRecording()}>Start</button>
      <button onClick={() => pauseRecording()}>Pause</button>
      <button onClick={() => stopRecording()}>Stop</button>
    </div>
  )
}
  • Custom Server (keep OpenAI API token secure)
import { useWhisper } from '@chengsokdara/use-whisper'

const App = () => {
  /**
   * you have more control like this
   * do whatever you want with the recorded speech
   * send it to your own custom server
   * and return the response back to useWhisper
   */
  const onTranscribe = (blob: Blob) => {
    const base64 = await new Promise<string | ArrayBuffer | null>(
      (resolve) => {
        const reader = new FileReader()
        reader.onloadend = () => resolve(reader.result)
        reader.readAsDataURL(blob)
      }
    )
    const body = JSON.stringify({ file: base64, model: 'whisper-1' })
    const headers = { 'Content-Type': 'application/json' }
    const { default: axios } = await import('axios')
    const response = await axios.post('/api/whisper', body, {
      headers,
    })
    const { text } = await response.data
    // you must return result from your server in Transcript format
    return {
      blob,
      text,
    }
  }

  const { transcript } = useWhisper({
    // callback to handle transcription with custom server
    onTranscribe,
  })

  return (
    <div>
      <p>{transcript.text}</p>
    </div>
  )
}

  • Examples

  • Real-time streaming trascription
import { useWhisper } from '@chengsokdara/use-whisper'

const App = () => {
  const { transcript } = useWhisper({
    apiKey: process.env.OPENAI_API_TOKEN, // YOUR_OPEN_AI_TOKEN
    streaming: true,
    timeSlice: 1_000, // 1 second
    whisperConfig: {
      language: 'en',
    },
  })

  return (
    <div>
      <p>{transcript.text}</p>
    </div>
  )
}
  • Remove silence before sending to Whisper to save cost
import { useWhisper } from '@chengsokdara/use-whisper'

const App = () => {
  const { transcript } = useWhisper({
    apiKey: process.env.OPENAI_API_TOKEN, // YOUR_OPEN_AI_TOKEN
    // use ffmpeg-wasp to remove silence from recorded speech
    removeSilence: true,
  })

  return (
    <div>
      <p>{transcript.text}</p>
    </div>
  )
}
  • Auto start recording on component mounted
import { useWhisper } from '@chengsokdara/use-whisper'

const App = () => {
  const { transcript } = useWhisper({
    apiKey: process.env.OPENAI_API_TOKEN, // YOUR_OPEN_AI_TOKEN
    // will auto start recording speech upon component mounted
    autoStart: true,
  })

  return (
    <div>
      <p>{transcript.text}</p>
    </div>
  )
}
  • Keep recording as long as the user is speaking
import { useWhisper } from '@chengsokdara/use-whisper'

const App = () => {
  const { transcript } = useWhisper({
    apiKey: process.env.OPENAI_API_TOKEN, // YOUR_OPEN_AI_TOKEN
    nonStop: true, // keep recording as long as the user is speaking
    stopTimeout: 5000, // auto stop after 5 seconds
  })

  return (
    <div>
      <p>{transcript.text}</p>
    </div>
  )
}
  • Customize Whisper API config when autoTranscribe is true
import { useWhisper } from '@chengsokdara/use-whisper'

const App = () => {
  const { transcript } = useWhisper({
    apiKey: process.env.OPENAI_API_TOKEN, // YOUR_OPEN_AI_TOKEN
    autoTranscribe: true,
    whisperConfig: {
      prompt: 'previous conversation', // you can pass previous conversation for context
      response_format: 'text', // output text instead of json
      temperature: 0.8, // random output
      language: 'es', // Spanish
    },
  })

  return (
    <div>
      <p>{transcript.text}</p>
    </div>
  )
}

  • Dependencies

    • @chengsokdara/react-hooks-async asynchronous react hooks
    • recordrtc: cross-browser audio recorder
    • lamejs encode wav into mp3 for cross-browser support
    • @ffmpeg/ffmpeg: for silence removal feature
    • hark: for speaking detection
    • axios: since fetch does not work with Whisper endpoint

most of these dependecies are lazy loaded, so it is only imported when it is needed

  • API

  • Config Object
Name Type Default Value Description
apiKey string '' your OpenAI API token
autoStart boolean false auto start speech recording on component mount
autoTranscribe boolean true should auto transcribe after stop recording
mode string transcriptions control Whisper mode either transcriptions or translations, currently only support translation to English
nonStop boolean false if true, record will auto stop after stopTimeout. However if user keep on speaking, the recorder will keep recording
removeSilence boolean false remove silence before sending file to OpenAI API
stopTimeout number 5,000 ms if nonStop is true, this become required. This control when the recorder auto stop
streaming boolean false transcribe speech in real-time based on timeSlice
timeSlice number 1000 ms interval between each onDataAvailable event
whisperConfig WhisperApiConfig undefined Whisper API transcription config
onDataAvailable (blob: Blob) => void undefined callback function for getting recorded blob in interval between timeSlice
onTranscribe (blob: Blob) => Promise<Transcript> undefined callback function to handle transcription on your own custom server
  • WhisperApiConfig
Name Type Default Value Description
prompt string undefined An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.
response_format string json The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
temperature number 0 The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.
language string en The language of the input audio. Supplying the input language in ISO-639-1 format will improve accuracy and latency.
  • Return Object
Name Type Description
recording boolean speech recording state
speaking boolean detect when user is speaking
transcribing boolean while removing silence from speech and send request to OpenAI Whisper API
transcript Transcript object return after Whisper transcription complete
pauseRecording Promise pause speech recording
startRecording Promise start speech recording
stopRecording Promise stop speech recording
  • Transcript
Name Type Description
blob Blob recorded speech in JavaScript Blob
text string transcribed text returned from Whisper API

  • Roadmap

    • react-native support, will be export as use-whisper/native

Contact me for web or mobile app development using React or React Native
https://chengsokdara.github.io