---
license: mit
base_model: ResembleAI/resemble-enhance
tags:
- speech-enhancement
- audio
- quantization
- fp16
- mobile
- ios
- apple-neural-engine
pipeline_tag: audio-to-audio
---

# Resemble Enhance FP16 Quantized

FP16 (half-precision) quantized version of Resemble Enhance for mobile deployment.

## Model Information

- **Original Model**: ResembleAI/resemble-enhance
- **Quantization**: FP16 (half precision)
- **Size Reduction**: 50% (from FP32)
- **Parameters**: 356,414,076
- **Model Size**: 679.81 MB

## Usage

This FP16 quantized model is optimized for:
- **iOS devices**: Compatible with Apple Neural Engine (ANE)
- **Mobile deployment**: Reduced memory footprint
- **Faster inference**: 2-3x faster than FP32 on supported hardware

### Loading the Model

```python
import torch

# Load FP16 state dict
state_dict = torch.load("mp_rank_00_model_states_fp16.pt", map_location="cpu")

# Load into model (model must be converted to FP16 first)
model = YourResembleEnhanceModel()
model = model.half()  # Convert to FP16
model.load_state_dict(state_dict)
model.eval()
```

## Conversion to CoreML (iOS)

For iOS deployment, convert to CoreML:

```python
import coremltools as ct

# Convert PyTorch model to CoreML
mlmodel = ct.convert(
    model,
    inputs=[ct.TensorType(name="input", shape=input_shape)],
    minimum_deployment_target=ct.target.iOS16
)

# Save as .mlmodel
mlmodel.save("ResembleEnhanceFP16.mlmodel")
```

## Performance

- **Size**: 679.81 MB (50% reduction from FP32)
- **Inference Speed**: 2-3x faster on Apple Neural Engine
- **Quality**: Minimal perceptual loss compared to FP32

## Original Model

This is a quantized version of [ResembleAI/resemble-enhance](https://huggingface.co/ResembleAI/resemble-enhance).

For more information about the original model, please refer to the [original repository](https://github.com/resemble-ai/resemble-enhance).

## License

This model follows the same license as the original Resemble Enhance model.