flutter_whisper 0.1.0

On-device speech-to-text transcription using whisper.cpp. Automatic model download, streaming segment results, Android support.

flutter_whisper

On-device speech-to-text transcription using whisper.cpp. No cloud, no API keys — models run locally on Android.

⚠️ Platform status: Android is fully supported (JNI + whisper.cpp via CMake). iOS is planned but not yet implemented.

Features

• 🎙️ On-device transcription (works offline, privacy-first)
• ⬇️ Automatic model download on first use (resumable, with integrity check)
• 🧠 5 model sizes: tiny (39 MB) → large (1.5 GB)
• 🌍 Multilingual (tiny/base are English-only, small+ are multilingual)
• ⏱️ Segment + word-level timestamps
• 📡 Streaming segment results (real-time UI updates)
• 🎤 Microphone recording → WAV → transcribe (Android)

Getting started

Add to pubspec.yaml:

dependencies:
  flutter_whisper: ^0.1.0

Usage

import 'package:flutter_whisper/flutter_whisper.dart';

final whisper = Whisper();

// 1. Initialize — downloads model on first use
await whisper.initialize(model: WhisperModel.tiny);

// 2. Transcribe a file
final result = await whisper.transcribeFile('/path/to/audio.wav');
print(result.text);          // "hello world"
print(result.language);      // "en"
for (final seg in result.segments) {
  print('[${seg.start}s - ${seg.end}s] ${seg.text}');
}

// 3. Record from the microphone, then transcribe
await whisper.startRecording();
// ... speak ...
final wavPath = await whisper.stopRecording();
final result2 = await whisper.transcribeFile(wavPath);

// 4. Clean up
await whisper.dispose();

Options

await whisper.initialize(
  model: WhisperModel.small,
  options: const WhisperOptions(
    language: 'en',        // '' = auto-detect
    translate: false,      // translate to English
    vad: true,             // filter silence
    wordTimestamps: true,  // word-level timestamps
    threads: 4,            // 0 = auto
  ),
);

Models

Model	Size	Languages	Use case
tiny	39 MB	English	Fastest, quick tests
base	75 MB	English	Fast + decent accuracy
small	150 MB	Multilingual	Good balance
medium	300 MB	Multilingual	High accuracy
large	1.5 GB	Multilingual	Best accuracy

Models download automatically from HuggingFace on first initialize().

Platform setup

Android — add RECORD_AUDIO permission (only if recording):

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

No network permissions needed — transcription runs on-device. Model downloads use the app's own network permission.

Example

Full demo app in example/:

cd example
flutter run

Additional information

• Report issues
• Native integration uses whisper.cpp via platform channels
• iOS support is on the roadmap (whisper.cpp static framework build)