Supported platforms #

Features #

• Automatic Speech Recognition integration for Flutter apps.

• Supports automatic model downloading and initialization. Can be configured to work fully offline by using assets models (see example folder).

• Seamless iOS and Android support with optimized performance.

• Can be configured to use specific language ("en", "fr", "de", etc) or auto-detect ("auto").

• Utilizes CORE ML for enhanced processing on iOS devices.

• Support for Large-v3-Turbo models (128 mel bands).

Installation #

To use this library in your Flutter project, follow these steps:

1. Add the library to your Flutter project's pubspec.yaml:

dependencies:
  whisper_ggml_plus: ^1.0.0

2. Run flutter pub get to install the package.

Usage #

To integrate Whisper ASR in your Flutter app:

1. Import the package:

import 'package:whisper_ggml_plus/whisper_ggml_plus.dart';

2. Run flutter pub get to install the package.

Usage #

To integrate Whisper ASR in your Flutter app:

1. Import the package:

import 'package:whisper_ggml_plus/whisper_ggml_plus.dart';

2. Pick your model. Smaller models are more performant, but the accuracy may be lower. Recommended models are tiny and small.

final model = WhisperModel.tiny;

3. Declare WhisperController and use it for transcription:

final controller = WhisperController();

final result = await controller.transcribe(
    model: model, /// Selected WhisperModel
    audioPath: audioPath, /// Path to .wav file
    lang: 'en', /// Language to transcribe
);

4. Use the result variable to access the transcription result:

if (result?.transcription.text != null) {
    /// Do something with the transcription
    print(result!.transcription.text);
}

Notes #

Transcription processing time is about 5x times faster when running in release mode.