Integrating OpenAI Whisper for On-Device Transcription in Flutter

Introduction

This tutorial shows how to integrate OpenAI Whisper for on-device transcription in Flutter mobile development. It focuses on practical architecture, audio pre-processing, and how to call a native Whisper engine from Flutter using MethodChannel or FFI. The goal is a reliable, privacy-preserving transcription flow that runs without network dependency.

Choosing A Whisper Model For Mobile

Whisper original models are large; for mobile you need quantized, smaller variants: ggml-quantized models used by whisper.cpp (tiny, base, small). The trade-offs are accuracy versus footprint and latency. For Android prefer lower-memory models (ggml-small-q4_0) or TFLite converted models with NNAPI/Metal delegates. On iOS, Metal/Accelerate delegates or running whisper.cpp via FFI are common. Decide by available memory, desired real-time capability, and acceptable latency.

Preparing Audio And Recording In Flutter

Whisper expects 16 kHz mono PCM 16-bit audio (depending on model conversion). Record at 16 kHz if possible, otherwise resample. Use a Flutter package such as record or flutter_sound to capture microphone input, then convert/normalize to 16-bit signed PCM mono.

Example: record, then read bytes and send to native engine via MethodChannel. The native side should accept raw PCM or WAV and perform any resampling necessary.

// Start recording and send bytes to native handler
final channel = MethodChannel('whisper/native');
await Record.start(sampleRate: 16000, encoder: AudioEncoder.pcm16);
final path = await Record.stop();
final data = await File(path).readAsBytes();
await channel.invokeMethod('transcribe', {'pcm': data});

Keep audio buffers small for streaming (e.g., 0.5–2s chunks). If you need truly low-latency streaming, implement a small ring buffer and call the native engine incrementally.

Integrating The Native Whisper Engine

There are two common approaches: compile whisper.cpp/ggml into a native library and call it via dart:ffi or implement a platform plugin and use MethodChannel.

• FFI: Best for performance and lower overhead. Expose C functions for init, feed_pcm, and get_result. Use dart:ffi to call these directly from Dart.

• MethodChannel: Simpler to implement quickly. Native Kotlin/Swift code receives byte arrays and calls the engine, returning transcripts.

Native responsibilities:

• Load the quantized ggml model and allocate memory conservatively.

• Initialize the decoder with desired language/options.

• Accept PCM frames, perform any necessary resampling and normalization.

• Run the model on chunks; return partial transcripts or final segments.

Error handling: preload model on background thread, detect OOM, and report friendly messages to Flutter. Provide cancellation tokens for in-flight transcriptions.

// Receive transcript from native and update UI
final result = await MethodChannel('whisper/native').invokeMethod('transcribeSync', {'path': path});
setState(() => transcript = result as String);

Performance Optimization And Memory Management

• Use quantized ggml models: q4_0/q4_1 dramatically reduce memory and CPU cost.

• Bind threads to background priorities; avoid running heavy inference on the UI thread.

• Use streaming to reduce peak memory usage; process short segments and free buffers immediately.

• On Android, leverage NNAPI or GPU delegates if you convert to TFLite/ONNX and need faster throughput.

• Measure: profile CPU, memory, and inference latency on target devices. Lower your beam size or use greedy decoding for faster results.

Privacy: on-device transcription avoids sending audio to the cloud but requires secure storage of models and careful permissions handling. Ask microphone permissions and explain usage in your privacy policy.

Cross-platform packaging: package native binaries for both architectures (arm64-v8a, armeabi-v7a, x86_64) and include them in the Flutter plugin or build scripts.

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Conclusion

Integrating Whisper on-device in Flutter requires choosing a mobile-friendly model, preparing audio to the required format, and invoking a native inference engine via FFI or MethodChannel. The main engineering work is around memory management, resampling/formatting audio, and threading. Start with a quantized ggml model and a simple MethodChannel flow to validate correctness, then move to FFI and streaming for improved performance. With careful model choice and optimizations, you can run accurate, private transcription entirely on-device in your Flutter mobile app.