flutter_whisper_kit 0.2.0

A Flutter plugin for on-device speech recognition and transcription using WhisperKit.

flutter_whisper_kit

A Flutter plugin that provides on-device speech recognition capabilities using WhisperKit. Achieve high-quality speech-to-text transcription while maintaining privacy.

日本語 README

Features

• 🔒 Complete On-Device Processing - No data is sent to external servers
• 🎯 High-Accuracy Speech Recognition - High-quality transcription with Whisper models
• 📱 Multiple Model Sizes - Choose from tiny to large based on your needs
• 🎙️ Real-Time Transcription - Convert microphone audio in real-time
• 📁 File-Based Transcription - Support for audio file transcription
• 📊 Progress Tracking - Monitor model download progress
• 🌍 Multi-Language Support - Supports 100+ languages
• ⚡ Type-Safe Error Handling - Safe error handling with Result type

Platform Support

Platform	Minimum Version	Status
iOS	16.0+	✅ Fully Supported
macOS	13.0+	✅ Fully Supported
Android	-	🚧 Planned for Future Release

Installation

Add to your pubspec.yaml:

dependencies:
  flutter_whisper_kit: ^0.2.0

iOS Configuration

Add these permissions to your iOS app's ios/Runner/Info.plist:

<key>NSMicrophoneUsageDescription</key>
<string>This app needs microphone access to record audio for speech transcription</string>
<key>NSDownloadsFolderUsageDescription</key>
<string>This app needs to access your Downloads folder to store WhisperKit models</string>
<key>NSDocumentsFolderUsageDescription</key>
<string>This app needs to access your Documents folder to store WhisperKit models</string>

macOS Configuration

Add these permissions to your macOS app's macos/Runner/Info.plist:

<key>NSMicrophoneUsageDescription</key>
<string>This app needs microphone access to record audio for speech transcription</string>
<key>NSLocalNetworkUsageDescription</key>
<string>This app needs to access your local network to download WhisperKit models</string>
<key>NSDownloadsFolderUsageDescription</key>
<string>This app needs to access your Downloads folder to store WhisperKit models</string>
<key>NSDocumentsFolderUsageDescription</key>
<string>This app needs to access your Documents folder to store WhisperKit models</string>

Also, ensure your macOS deployment target is set to 13.0 or higher in macos/Runner.xcodeproj/project.pbxproj:

MACOSX_DEPLOYMENT_TARGET = 13.0;

Usage

Basic Usage

import 'package:flutter_whisper_kit/flutter_whisper_kit.dart';

// Create an instance of the plugin
final whisperKit = FlutterWhisperKit();

// Load a model
final result = await whisperKit.loadModel(
  'tiny',  // Model size: tiny, base, small, medium, large-v2, large-v3
  modelRepo: 'argmaxinc/whisperkit-coreml',
);
print('Model loaded: $result');

// Transcribe from audio file
final transcription = await whisperKit.transcribeFromFile(
  '/path/to/audio/file.mp3',
  options: DecodingOptions(
    task: DecodingTask.transcribe,
    language: 'en',  // Specify language (null for auto-detection)
  ),
);
print('Transcription: ${transcription?.text}');

Real-Time Transcription

// Listen to transcription stream
whisperKit.transcriptionStream.listen((transcription) {
  print('Real-time transcription: ${transcription.text}');
});

// Start recording
await whisperKit.startRecording(
  options: DecodingOptions(
    task: DecodingTask.transcribe,
    language: 'en',
  ),
);

// Stop recording
final finalTranscription = await whisperKit.stopRecording();
print('Final transcription: ${finalTranscription?.text}');

Error Handling (Result Type)

Since v0.2.0, Result type APIs have been added for safer error handling:

// Load model with Result type
final loadResult = await whisperKit.loadModelWithResult(
  'tiny',
  modelRepo: 'argmaxinc/whisperkit-coreml',
);

loadResult.when(
  success: (modelPath) {
    print('Model loaded successfully: $modelPath');
  },
  failure: (error) {
    print('Model loading failed: ${error.message}');
    // Handle errors by error code
    switch (error.code) {
      case WhisperKitErrorCode.modelNotFound:
        // Handle model not found
        break;
      case WhisperKitErrorCode.networkError:
        // Handle network error
        break;
      default:
        // Handle other errors
    }
  },
);

// Transcribe with Result type
final transcribeResult = await whisperKit.transcribeFileWithResult(
  audioPath,
  options: DecodingOptions(language: 'en'),
);

// Handle success/failure with fold method
final text = transcribeResult.fold(
  onSuccess: (result) => result?.text ?? 'No result',
  onFailure: (error) => 'Error: ${error.message}',
);

Model Management

// Fetch available models
final models = await whisperKit.fetchAvailableModels(
  modelRepo: 'argmaxinc/whisperkit-coreml',
);

// Get recommended models
final recommended = await whisperKit.recommendedModels();
print('Recommended model: ${recommended?.defaultModel}');

// Download model with progress
await whisperKit.download(
  variant: 'base',
  repo: 'argmaxinc/whisperkit-coreml',
  onProgress: (progress) {
    print('Download progress: ${(progress.fractionCompleted * 100).toStringAsFixed(1)}%');
  },
);

// Monitor model progress stream
whisperKit.modelProgressStream.listen((progress) {
  print('Model progress: ${progress.fractionCompleted * 100}%');
});

Language Detection

// Detect language from audio file
final detection = await whisperKit.detectLanguage(audioPath);
print('Detected language: ${detection?.language}');
print('Confidence: ${detection?.probabilities[detection.language]}');

Advanced Configuration

// Custom decoding options
final options = DecodingOptions(
  verbose: true,                        // Enable verbose logging
  task: DecodingTask.transcribe,       // transcribe or translate
  language: 'en',                       // Language code (null for auto-detection)
  temperature: 0.0,                     // Sampling temperature (0.0-1.0)
  temperatureFallbackCount: 5,          // Temperature fallback count
  wordTimestamps: true,                 // Enable word timestamps
  chunkingStrategy: ChunkingStrategy.vad, // Chunking strategy
);

// Detailed transcription results
final result = await whisperKit.transcribeFromFile(audioPath, options: options);
if (result != null) {
  print('Text: ${result.text}');
  print('Language: ${result.language}');
  
  // Segment information
  for (final segment in result.segments) {
    print('Segment ${segment.id}: ${segment.text}');
    print('  Start: ${segment.startTime}s, End: ${segment.endTime}s');
    
    // Word timing information (if wordTimestamps: true)
    for (final word in segment.words) {
      print('  Word: ${word.word} (${word.start}s - ${word.end}s)');
    }
  }
}

Model Size Selection

Choose the appropriate model size based on your use case:

Model	Size	Speed	Accuracy	Use Case
tiny	~39MB	Very Fast	Low	Real-time processing, battery-conscious
tiny-en	~39MB	Very Fast	Low (English only)	English-only real-time processing
base	~145MB	Fast	Medium	Balanced performance
small	~466MB	Medium	High	When higher accuracy is needed
medium	~1.5GB	Slow	Higher	When even higher accuracy is needed
large-v2	~2.9GB	Very Slow	Very High	When maximum accuracy is needed
large-v3	~2.9GB	Very Slow	Highest	Latest and highest accuracy

Example App

The example folder contains a sample app that demonstrates all features:

cd packages/flutter_whisper_kit/example
flutter run

Troubleshooting

Build errors on iOS/macOS

1. Check minimum deployment target (iOS 16.0+, macOS 13.0+)
2. Update to the latest Xcode
3. Run pod install

Model download fails

1. Check network connection
2. Ensure sufficient storage space
3. Try redownload: true option

Low transcription accuracy

1. Try a larger model size
2. Explicitly specify language with language parameter
3. Adjust temperature parameter (0.0 for more deterministic, 1.0 for more creative)

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.

Acknowledgments

This plugin is based on WhisperKit. Thanks to the Argmax Inc. team for providing this excellent library.