React Native binding of whisper.cpp.

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

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)

npm install whisper.rn

Please re-run npx pod-install again.

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

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

# whisper.rn
-keep class com.rnwhisper.** { *; }

For build, 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.

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

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

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

<key>NSMicrophoneUsageDescription</key>
<string>This app requires microphone access in order to transcribe speech</string>

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

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

<uses-permission android:name="android.permission.RECORD_AUDIO" />

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

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)

Use realtime transcribe:

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

subscribe(evt => {
  const { isCapturing, data, processTime, recordingTime } = evt
  console.log(
    `Realtime transcribing: ${isCapturing ? 'ON' : 'OFF'}\n` +
      // The inference text result from audio record:
      `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:

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:

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.

Please visit the Documentation for more details.

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

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:

// ...
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.

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):

[
  '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.

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.

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.

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

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

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

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

MIT

Made with create-react-native-library

Built and maintained by BRICKS.