@cloudraker/use-whisper
Version:
React Hook for OpenAI Whisper API with speech recorder and silence removal built-in.
278 lines (220 loc) • 12.5 kB
Markdown
# useWhisper
React Hook for OpenAI Whisper API with speech recorder, real-time transcription, and silence removal built-in. This project is a fork of the [chengsokdara/use-whisper](https://github.com/chengsokdara/use-whisper) project, which doesn't seem to be maintained anymore.
---
- ### Install
```
npm i @cloudraker/use-whisper
```
```
yarn add @cloudraker/use-whisper
```
- ### Usage
Below is an example, that should likely not be used in production, as it exposes your OpenAI API token. See below to use a proxy, which can include your own deployment of Whisper.
```jsx
import { useWhisper } from '@cloudraker/use-whisper'
const App = () => {
const {
recording,
speaking,
transcribing,
transcript,
pauseRecording,
startRecording,
stopRecording,
} = useWhisper({
apiKey: process.env.OPENAI_API_TOKEN, // YOUR_OPEN_AI_TOKEN, ⚠️ this is dangerous to expose front-end
})
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>
)
}
```
- ###### Request Proxy (preventing your API key from being exposed client side)
```jsx
import { useWhisper } from '@cloudraker/use-whisper'
const App = () => {
const { transcript } = useWhisper({
// callback to handle transcription with custom server
whisperApiEndpoints: {
transcriptions: `https://your-custom-endpoint/api/whisper/transcriptions`,
translations: `https://your-custom-endpoint/api/whisper/translations`,
},
})
return (
<div>
<p>{transcript.text}</p>
</div>
)
}
```
And here's a sample server implementation for transcriptions (Next.js 15 with App router), proxying the requests to OpenAI
```ts
import { NextRequest, NextResponse } from 'next/server';
export async function POST(req: NextRequest) {
const apiKey = process.env.OPENAI_API_KEY;
const whisperApiUrl = 'https://api.openai.com/v1/audio/transcriptions';
if (!apiKey) {
return NextResponse.json({ error: 'OPENAI_API_KEY is not set' }, { status: 500 });
}
try {
const formData = await req.formData();
const response = await fetch(whisperApiUrl, {
method: 'POST',
headers: {
'Authorization': `Bearer ${apiKey}`,
},
body: formData,
});
const data = await response.json();
return NextResponse.json(data, { status: response.status });
} catch (error) {
console.error(error);
return NextResponse.json({ error: 'Failed to proxy request' }, { status: 500 });
}
}
```
- ### Examples
- ###### Real-time streaming transcription
```jsx
import { useWhisper } from '@cloudraker/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
```jsx
import { useWhisper } from '@cloudraker/use-whisper'
const App = () => {
const { transcript } = useWhisper({
// use ffmpeg-wasp to remove silence from recorded speech
removeSilence: true,
})
return (
<div>
<p>{transcript.text}</p>
</div>
)
}
```
- ###### Auto start recording on component mounted
```jsx
import { useWhisper } from '@cloudraker/use-whisper'
const App = () => {
const { transcript } = useWhisper({
// 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
```jsx
import { useWhisper } from '@cloudraker/use-whisper'
const App = () => {
const { transcript } = useWhisper({
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
```jsx
import { useWhisper } from '@cloudraker/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
- **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](#whisperapiconfig) | undefined | Whisper API transcription config |
| whisperApiEndpoint | string | undefined | optional endpoints for the Whisper API (e.g., { transcriptions: '', translations: '' }), useful for proxy requests |
| onDataAvailable | (blob: Blob) => void | undefined | callback function for getting recorded blob in interval between timeSlice |
| onTranscribe | (blob: Blob) => Promise<[Transcript](#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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) 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](#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](https://chengsokdara.github.io)