whisper.rn


React Native binding of whisper.cpp.

whisper.cpp: High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model

Screenshots

| | |
| :------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: |
| iOS: Tested on iPhone 13 Pro Max | Android: Tested on Pixel 6 |
| (tiny.en, Core ML enabled, release mode + archive) | (tiny.en, armv8.2-a+fp16, release mode) |

Installation

``sh
npm install whisper.rn
`

#### iOS

Please re-run npx pod-install again.

By default, whisper.rn will use pre-built rnwhisper.xcframework for iOS. If you want to build from source, please set RNWHISPER_BUILD_FROM_SOURCE to 1 in your Podfile.

If you want to use medium or large model, the Extended Virtual Addressing capability is recommended to enable on iOS project.

#### Android

Add proguard rule if it's enabled in project (android/app/proguard-rules.pro):

`proguard


whisper.rn


-keep class com.rnwhisper.* { ; }
`

It's recommended to use ndkVersion = "24.0.8215888" (or above) in your root project build configuration for Apple Silicon Macs. Otherwise please follow this trobleshooting issue.

#### Expo

You will need to prebuild the project before using it. See Expo guide for more details.

Add Microphone Permissions (Optional)

If you want to use realtime transcribe, you need to add the microphone permission to your app.

$3

Add these lines to ios/[YOU_APP_NAME]/info.plist

`xml
NSMicrophoneUsageDescription
This app requires microphone access in order to transcribe speech
`

For tvOS, please note that the microphone is not supported.

$3

Add the following line to android/app/src/main/AndroidManifest.xml

`xml

`

Tips & Tricks

The Tips & Tricks document is a collection of tips and tricks for using whisper.rn.

Usage

`js
import { initWhisper } from 'whisper.rn'

const whisperContext = await initWhisper({
filePath: 'file://.../ggml-tiny.en.bin',
})

const sampleFilePath = 'file://.../sample.wav'
const options = { language: 'en' }
const { stop, promise } = whisperContext.transcribe(sampleFilePath, options)

const { result } = await promise
// result: (The inference text result from audio file)
`

Voice Activity Detection (VAD)

Voice Activity Detection allows you to detect speech segments in audio data using the Silero VAD model.

#### Initialize VAD Context

`typescript
import { initWhisperVad } from 'whisper.rn'

const vadContext = await initWhisperVad({
filePath: require('./assets/ggml-silero-v6.2.0.bin'), // VAD model file
useGpu: true, // Use GPU acceleration (iOS only)
nThreads: 4, // Number of threads for processing
})
`

#### Detect Speech Segments

##### From Audio Files

`typescript
// Detect speech in audio file (supports same formats as transcribe)
const segments = await vadContext.detectSpeech(require('./assets/audio.wav'), {
threshold: 0.5, // Speech probability threshold (0.0-1.0)
minSpeechDurationMs: 250, // Minimum speech duration in ms
minSilenceDurationMs: 100, // Minimum silence duration in ms
maxSpeechDurationS: 30, // Maximum speech duration in seconds
speechPadMs: 30, // Padding around speech segments in ms
samplesOverlap: 0.1, // Overlap between analysis windows
})

// Also supports:
// - File paths: vadContext.detectSpeech('path/to/audio.wav', options)
// - HTTP URLs: vadContext.detectSpeech('https://example.com/audio.wav', options)
// - Base64 WAV: vadContext.detectSpeech('data:audio/wav;base64,...', options)
// - Assets: vadContext.detectSpeech(require('./assets/audio.wav'), options)
`

##### From Raw Audio Data

`typescript
// Detect speech in base64 encoded float32 PCM data
const segments = await vadContext.detectSpeechData(base64AudioData, {
threshold: 0.5,
minSpeechDurationMs: 250,
minSilenceDurationMs: 100,
maxSpeechDurationS: 30,
speechPadMs: 30,
samplesOverlap: 0.1,
})
`

#### Process Results

`typescript
segments.forEach((segment, index) => {
console.log(
Segment ${index + 1}: ${segment.t0.toFixed(2)}s - ${segment.t1.toFixed(
2,
)}s,
)
console.log(Duration: ${(segment.t1 - segment.t0).toFixed(2)}s)
})
`

#### Release VAD Context

`typescript
await vadContext.release()
// Or release all VAD contexts
await releaseAllWhisperVad()
`

Realtime Transcription

The new RealtimeTranscriber provides enhanced realtime transcription with features like Voice Activity Detection (VAD), auto-slicing, and memory management.

`js
// If your RN packager is not enable package exports support, use whisper.rn/src/realtime-transcription
import { RealtimeTranscriber } from 'whisper.rn/realtime-transcription'
import { AudioPcmStreamAdapter } from 'whisper.rn/realtime-transcription/adapters'
import RNFS from 'react-native-fs' // or any compatible filesystem

// Dependencies
const whisperContext = await initWhisper({
/ ... /
})
const vadContext = await initWhisperVad({
/ ... /
})
const audioStream = new AudioPcmStreamAdapter() // requires @fugood/react-native-audio-pcm-stream

// Create transcriber
const transcriber = new RealtimeTranscriber(
{ whisperContext, vadContext, audioStream, fs: RNFS },
{
audioSliceSec: 30,
vadPreset: 'default',
autoSliceOnSpeechEnd: true,
transcribeOptions: { language: 'en' },
},
{
onTranscribe: (event) => console.log('Transcription:', event.data?.result),
onVad: (event) => console.log('VAD:', event.type, event.confidence),
onStatusChange: (isActive) =>
console.log('Status:', isActive ? 'ACTIVE' : 'INACTIVE'),
onError: (error) => console.error('Error:', error),
},
)

// Start/stop transcription
await transcriber.start()
await transcriber.stop()
`

Dependencies:

- @fugood/react-native-audio-pcm-stream for AudioPcmStreamAdapter
- Compatible filesystem module (e.g., react-native-fs). See filesystem interface for TypeScript definition

Custom Audio Adapters:
You can create custom audio stream adapters by implementing the AudioStreamInterface. This allows integration with different audio sources or custom audio processing pipelines.

Example: See complete example for full implementation including file simulation and UI.

Please visit the Documentation for more details.

Usage with assets

You can also use the model file / audio file from assets:

`js
import { initWhisper } from 'whisper.rn'

const whisperContext = await initWhisper({
filePath: require('../assets/ggml-tiny.en.bin'),
})

const { stop, promise } = whisperContext.transcribe(
require('../assets/sample.wav'),
options,
)

// ...
`

This requires editing the metro.config.js to support assets:

`js
// ...
const defaultAssetExts = require('metro-config/src/defaults/defaults').assetExts

module.exports = {
// ...
resolver: {
// ...
assetExts: [
...defaultAssetExts,
'bin', // whisper.rn: ggml model binary
'mil', // whisper.rn: CoreML model asset
],
},
}
`

Please note that:

- It will significantly increase the size of the app in release mode.
- The RN packager is not allowed file size larger than 2GB, so it not able to use original f16 large model (2.9GB), you can use quantized models instead.

Core ML support

_Platform: iOS 15.0+, tvOS 15.0+_

To use Core ML on iOS, you will need to have the Core ML model files.

The .mlmodelc model files is load depend on the ggml model file path. For example, if your ggml model path is ggml-tiny.en.bin, the Core ML model path will be ggml-tiny.en-encoder.mlmodelc. Please note that the ggml model is still needed as decoder or encoder fallback.

The Core ML models are hosted here: https://huggingface.co/ggerganov/whisper.cpp/tree/main

If you want to download model at runtime, during the host file is archive, you will need to unzip the file to get the .mlmodelc directory, you can use library like react-native-zip-archive, or host those individual files to download yourself.

The .mlmodelc is a directory, usually it includes 5 files (3 required):

`json5
[
'model.mil',
'coremldata.bin',
'weights/weight.bin',
// Not required:
// 'metadata.json', 'analytics/coremldata.bin',
]
`

Or just use require to bundle that in your app, like the example app does, but this would increase the app size significantly.

`js
const whisperContext = await initWhisper({
filePath: require('../assets/ggml-tiny.en.bin')
coreMLModelAsset:
Platform.OS === 'ios'
? {
filename: 'ggml-tiny.en-encoder.mlmodelc',
assets: [
require('../assets/ggml-tiny.en-encoder.mlmodelc/weights/weight.bin'),
require('../assets/ggml-tiny.en-encoder.mlmodelc/model.mil'),
require('../assets/ggml-tiny.en-encoder.mlmodelc/coremldata.bin'),
],
}
: undefined,
})
`

In real world, we recommended to split the asset imports into another platform specific file (e.g. context-opts.ios.js) to avoid these unused files in the bundle for Android.

Run with example

The example app provide a simple UI for testing the functions.

Used Whisper model: tiny.en in https://huggingface.co/ggerganov/whisper.cpp
Sample file: jfk.wav in https://github.com/ggerganov/whisper.cpp/tree/master/samples

Please follow the Development Workflow section of contributing guide to run the example app.

Mock whisper.rn

We have provided a mock version of whisper.rn for testing purpose you can use on Jest:

`js
jest.mock('whisper.rn', () => require('whisper.rn/jest-mock'))
`

Deprecated APIs

$3

> ⚠️ Deprecated: Use RealtimeTranscriber instead for enhanced features and better performance.

`js
const { stop, subscribe } = await whisperContext.transcribeRealtime(options)

subscribe((evt) => {
const { isCapturing, data, processTime, recordingTime } = evt
console.log(
Realtime transcribing: ${isCapturing ? 'ON' : 'OFF'}\n +
Result: ${data.result}\n\n +
Process time: ${processTime}ms\n +
Recording time: ${recordingTime}ms,
)
if (!isCapturing) console.log('Finished realtime transcribing')
})
`

In iOS, You may need to change the Audio Session so that it can be used with other audio playback, or to optimize the quality of the recording. So we have provided AudioSession utilities for you:

Option 1 - Use options in transcribeRealtime:

`js
import { AudioSessionIos } from 'whisper.rn'

const { stop, subscribe } = await whisperContext.transcribeRealtime({
audioSessionOnStartIos: {
category: AudioSessionIos.Category.PlayAndRecord,
options: [AudioSessionIos.CategoryOption.MixWithOthers],
mode: AudioSessionIos.Mode.Default,
},
audioSessionOnStopIos: 'restore', // Or an AudioSessionSettingIos
})
`

Option 2 - Manage the Audio Session in anywhere:

`js
import { AudioSessionIos } from 'whisper.rn'

await AudioSessionIos.setCategory(AudioSessionIos.Category.PlayAndRecord, [
AudioSessionIos.CategoryOption.MixWithOthers,
])
await AudioSessionIos.setMode(AudioSessionIos.Mode.Default)
await AudioSessionIos.setActive(true)
// Then you can start do recording
`

In Android, you may need to request the microphone permission by PermissionAndroid.

Apps using whisper.rn

- BRICKS: Our product for building interactive signage in simple way. We provide LLM functions as Generator LLM/Assistant.
- ... (Any Contribution is welcome)

Node.js binding

- whisper.node: An another Node.js binding of whisper.cpp but made API same as whisper.rn.

Contributing

See the contributing guide to learn how to contribute to the repository and the development workflow.

Troubleshooting

See the troubleshooting if you encounter any problem while using whisper.rn`.

License

MIT

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Made with create-react-native-library

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Built and maintained by BRICKS.