> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runanywhere.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# STT Options

> Configure speech-to-text behavior

Configure STT behavior for different use cases and languages.

## Model Selection

Choose the right model based on your needs:

| Model              | Size    | Languages | Quality | Speed   | Use Case          |
| ------------------ | ------- | --------- | ------- | ------- | ----------------- |
| `whisper-tiny.en`  | \~75MB  | English   | Good    | Fastest | Quick commands    |
| `whisper-base.en`  | \~150MB | English   | Better  | Fast    | General use       |
| `whisper-small.en` | \~250MB | English   | Best    | Medium  | Accuracy-critical |
| `whisper-tiny`     | \~75MB  | Multi     | Good    | Fast    | Multilingual apps |

## Register Multiple Models

```dart theme={null}
// English-optimized for primary use
Onnx.addModel(
  id: 'whisper-tiny-en',
  name: 'Whisper Tiny English',
  url: 'https://github.com/RunanywhereAI/sherpa-onnx/releases/download/runanywhere-models-v1/sherpa-onnx-whisper-tiny.en.tar.gz',
  modality: ModelCategory.speechRecognition,
);

// Multilingual for international users
Onnx.addModel(
  id: 'whisper-tiny-multi',
  name: 'Whisper Tiny Multilingual',
  url: 'https://github.com/RunanywhereAI/sherpa-onnx/releases/download/runanywhere-models-v1/sherpa-onnx-whisper-tiny.tar.gz',
  modality: ModelCategory.speechRecognition,
);
```

## Switch Models at Runtime

```dart theme={null}
// Load English model by default
await RunAnywhere.loadSTTModel('whisper-tiny-en');

// Switch to multilingual when needed
Future<void> switchToMultilingual() async {
  await RunAnywhere.unloadSTTModel();
  await RunAnywhere.loadSTTModel('whisper-tiny-multi');
}
```

## Memory Management

Unload STT model when not needed to free memory:

```dart theme={null}
// Check if loaded
if (RunAnywhere.isSTTModelLoaded) {
  print('Current STT model: ${RunAnywhere.currentSTTModelId}');
}

// Unload to free memory
await RunAnywhere.unloadSTTModel();
```

## Audio Preprocessing Tips

<AccordionGroup>
  <Accordion title="Sample Rate Conversion" icon="wave-square">
    If your audio isn't 16kHz, convert it before transcription: `dart // Example: Convert 44.1kHz
            to 16kHz // Use a package like 'flutter_sound' for resampling `
  </Accordion>

  <Accordion title="Noise Reduction" icon="volume-xmark">
    For noisy environments, consider preprocessing audio: - Apply a high-pass filter to remove
    low-frequency noise - Normalize audio levels - Remove silence at beginning/end
  </Accordion>

  <Accordion title="Audio Format" icon="file-audio">
    Always ensure correct format: - PCM16 (16-bit signed integer) - 16,000 Hz sample rate - Mono
    (single channel)
  </Accordion>
</AccordionGroup>

## Error Handling

```dart theme={null}
try {
  final text = await RunAnywhere.transcribe(audioBytes);
  print('Transcribed: $text');
} on SDKError catch (e) {
  switch (e.type) {
    case SDKErrorType.sttNotAvailable:
      print('STT not available. Load an STT model first.');
      break;
    case SDKErrorType.componentNotReady:
      print('STT model not loaded.');
      break;
    default:
      print('STT error: ${e.message}');
  }
}
```

## Best Practices

<Tip>
  **Start with the smallest model** that meets your accuracy needs. You can always upgrade later if
  needed.
</Tip>

1. **Preload during idle time** — Download and load STT model before user needs it
2. **Use English-specific models** — They're smaller and more accurate for English
3. **Handle empty audio** — Check audio length before transcribing
4. **Provide feedback** — Show transcription progress to users

## See Also

<CardGroup cols={2}>
  <Card title="transcribe()" icon="microphone" href="/flutter/stt/transcribe">
    Basic transcription
  </Card>

  <Card title="Voice Agent" icon="robot" href="/flutter/voice-agent">
    Complete voice pipeline
  </Card>
</CardGroup>