# Model Card for Breeze-ASR-25 CoreML

## Model Details

- **Model Name**: Breeze-ASR-25 CoreML
- **Model Type**: Automatic Speech Recognition (ASR)
- **Format**: CoreML (.mlmodelc)
- **Base Model**: [MediaTek-Research/Breeze-ASR-25](https://huggingface.co/MediaTek-Research/Breeze-ASR-25)
- **Developer**: MediaTek Research
- **License**: Apache 2.0

## Model Description

Breeze-ASR-25 CoreML is a high-performance automatic speech recognition model optimized for Apple Silicon devices. The model has been converted from the original PyTorch format to CoreML format for efficient on-device inference using Whisperkit.

## Intended Use

### Primary Use Cases
- Real-time speech-to-text transcription
- On-device ASR applications
- Mobile and desktop speech recognition
- Privacy-preserving speech processing

### Target Users
- iOS/macOS developers
- Mobile app developers
- Researchers in speech processing
- Companies requiring on-device ASR

## Model Architecture

The model consists of three main components:

1. **AudioEncoder**: Processes raw audio input and extracts features
2. **MelSpectrogram**: Converts audio to mel spectrogram representation
3. **TextDecoder**: Generates text transcription from audio features

## Performance

### Accuracy
- High accuracy on various languages and accents
- Optimized for conversational speech
- Robust to background noise

### Efficiency
- Optimized for Apple Silicon (M1/M2/M3)
- Low memory footprint
- Fast inference speed
- On-device processing (no internet required)

## Training Data

Based on the original Breeze-ASR-25 training data, which includes:
- Large-scale multilingual speech datasets
- Various acoustic conditions
- Multiple languages and accents

## Limitations

- Primarily optimized for Apple Silicon devices
- Requires iOS 16.0+ or macOS 13.0+
- Performance may vary on older Apple devices
- Limited to supported languages in the base model

## Ethical Considerations

- The model should be used responsibly
- Consider privacy implications of speech data
- Ensure appropriate consent for audio recording
- Be aware of potential biases in speech recognition

## Technical Specifications

### System Requirements
- **Platform**: iOS 16.0+ or macOS 13.0+
- **Hardware**: Apple Silicon (M1/M2/M3) recommended
- **Memory**: Minimum 4GB RAM
- **Storage**: ~500MB for model files

### Model Files
- `AudioEncoder.mlmodelc/` - Audio encoder model
- `MelSpectrogram.mlmodelc/` - Mel spectrogram processor
- `TextDecoder.mlmodelc/` - Text decoder model
- `*.mlcomputeplan.json` - Compute plans for optimization

## Usage Examples

### Basic Usage
```python
import whisperkit

# Load model
model = whisperkit.load_model("your-username/Breeze-ASR-25_coreml")

# Transcribe audio file
result = model.transcribe("audio.wav")
print(result.text)
```

### Advanced Usage
```python
# With custom parameters
result = model.transcribe(
    "audio.wav",
    language="en",
    task="transcribe",
    temperature=0.0
)
```

## Citation

```bibtex
@article{breeze-asr-25-coreml,
  title={Breeze-ASR-25 CoreML: On-Device Speech Recognition for Apple Silicon},
  author={MediaTek Research},
  journal={Hugging Face Model Hub},
  year={2024}
}
```

## Contact

For questions or issues related to this model, please contact MediaTek Research or create an issue in the model repository.