> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/argmaxinc/WhisperKit/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> On-device speech recognition and text-to-speech for Apple Silicon

WhisperKit is an [Argmax](https://www.takeargmax.com) framework for deploying state-of-the-art speech-to-text systems on Apple devices with advanced features like real-time streaming, word timestamps, voice activity detection, and more.

## What is WhisperKit?

WhisperKit brings powerful speech processing capabilities directly to Apple devices:

* **WhisperKit**: Speech-to-text powered by OpenAI's Whisper models, optimized for on-device inference
* **TTSKit**: Text-to-speech using Qwen3 TTS models for natural-sounding voice synthesis

Both frameworks run entirely on-device using Core ML, ensuring privacy and low latency without requiring network connectivity.

## Key Features

### WhisperKit (Speech Recognition)

* **Real-time streaming transcription** with live audio input
* **Word-level timestamps** for precise alignment
* **Voice activity detection (VAD)** to optimize processing
* **Multilingual support** with automatic language detection
* **Flexible model selection** from tiny to large-v3 variants
* **Custom model deployment** via HuggingFace integration
* **OpenAI-compatible API server** for easy integration

### TTSKit (Text-to-Speech)

* **Real-time streaming playback** as audio generates
* **9 built-in voices** with natural prosody
* **10 language support** including English, Chinese, Japanese, Korean
* **Style instructions** for controlling speech characteristics (1.7B model)
* **Automatic text chunking** for long-form content
* **Concurrent generation** for improved throughput
* **Prompt caching** for 90% faster subsequent generations

## Use Cases

<CardGroup cols={2}>
  <Card title="Live Transcription" icon="microphone">
    Real-time meeting transcription, lecture notes, accessibility features
  </Card>

  <Card title="Voice Commands" icon="waveform-lines">
    On-device voice control with low latency and full privacy
  </Card>

  <Card title="Content Creation" icon="headphones">
    Generate narration, audiobooks, and voiceovers without cloud services
  </Card>

  <Card title="Accessibility" icon="universal-access">
    Text-to-speech for screen readers, speech-to-text for hearing assistance
  </Card>
</CardGroup>

## Platform Support

### WhisperKit

* iOS 16.0+
* macOS 13.0+
* watchOS 10.0+
* visionOS 1.0+

### TTSKit

* iOS 18.0+
* macOS 15.0+

## Architecture

WhisperKit uses a modular, protocol-based architecture where each component can be swapped:

```swift theme={null}
public var audioProcessor: any AudioProcessing
public var featureExtractor: any FeatureExtracting
public var audioEncoder: any AudioEncoding
public var textDecoder: any TextDecoding
public var segmentSeeker: any SegmentSeeking
```

This design allows you to customize individual components while maintaining compatibility with the rest of the pipeline.

## Model Variants

### WhisperKit Models

Models are automatically recommended based on your device capabilities:

* `tiny` - Fastest, lowest accuracy (\~150 MB)
* `base` - Good balance for mobile devices (\~290 MB)
* `small` - Better accuracy, still fast (\~967 MB)
* `medium` - High accuracy (\~3.1 GB)
* `large-v3` - Best accuracy (\~6.2 GB)
* `distil-large-v3` - Distilled version with similar accuracy to large-v3 but faster

### TTSKit Models

* `qwen3TTS_0_6b` - Fast, runs on all platforms (\~1 GB)
* `qwen3TTS_1_7b` - Higher quality, macOS only, supports style instructions (\~2.2 GB)

## Performance

WhisperKit is optimized for Apple Silicon with:

* **Neural Engine acceleration** for maximum performance
* **Automatic compute unit selection** based on device capabilities
* **Model specialization** for reduced latency on first run
* **Efficient memory usage** with streaming processing

<Note>
  For production deployments requiring real-time transcription and speaker diarization at scale, consider [Argmax Pro SDK](https://www.argmaxinc.com/#SDK) with 9x faster models and advanced features.
</Note>

## Open Source

WhisperKit is open source under the MIT License:

* [GitHub Repository](https://github.com/argmaxinc/WhisperKit)
* [Python Tools](https://github.com/argmaxinc/whisperkittools) for model conversion
* [Benchmarks & Device Support](https://huggingface.co/spaces/argmaxinc/whisperkit-benchmarks)
* [Discord Community](https://discord.gg/G5F5GZGecC)

## Next Steps

<CardGroup cols={2}>
  <Card title="Installation" icon="download" href="/installation">
    Add WhisperKit to your Xcode project
  </Card>

  <Card title="Quick Start" icon="rocket" href="/quickstart">
    Get started with your first transcription and TTS generation
  </Card>
</CardGroup>