---
license: mit
tags:
  - speech-enhancement
  - noise-reduction
  - coreml
  - apple-neural-engine
  - deepfilternet
language:
  - en
  - multilingual
library_name: qwen3-asr-swift
pipeline_tag: audio-to-audio
---

# DeepFilterNet3 - Core ML

Speech enhancement (noise removal) model converted to Core ML for Apple Neural Engine inference.

Based on [DeepFilterNet3](https://github.com/Rikorose/DeepFilterNet) (Interspeech 2023).

## Model Details

| Property | Value |
|---|---|
| Parameters | 2.1M |
| Model size | 4.2 MB |
| Sample rate | 48 kHz |
| Latency | ~40ms (20ms frame + lookahead) |
| PESQ (DNS4) | 3.17 |
| Compute target | Apple Neural Engine |
| Framework | Core ML (mlprogram) |
| Min deployment | macOS 14+ / iOS 17+ |

## Architecture

Signal processing (STFT, ERB filterbank, deep filtering) runs on CPU via Accelerate/vDSP.
Neural network inference runs on the Neural Engine via Core ML.

- Encoder: 4x SepConv2d + SqueezedGRU (256-dim, 3 layers)
- ERB Decoder: SqueezedGRU + skip convs + sigmoid mask (32 bands)
- DF Decoder: SqueezedGRU + deep filter coefficients (96 bins x 5 taps)

## Usage with qwen3-asr-swift

```swift
import SpeechEnhancement

let enhancer = try await SpeechEnhancer.fromPretrained()
let cleanAudio = try enhancer.enhance(audio: noisyAudio, sampleRate: 48000)
```

CLI:

```bash
audio denoise input.wav --output clean.wav
```

## Performance

| Metric | Value |
|---|---|
| RTF (M2 Max) | 0.34 (3x real-time) |
| 20s audio | ~7s processing |

## Files

- `DeepFilterNet3.mlpackage/` - Core ML model (Neural Engine)
- `auxiliary.npz` - Signal processing data (ERB filterbank, Vorbis window, normalization states)

## Conversion

Converted from PyTorch checkpoint using `scripts/convert_deepfilternet3.py` in [qwen3-asr-swift](https://github.com/AufKlworworworworworkwork/qwen3-asr-swift).

## License

MIT (following DeepFilterNet3 original license)

## Citation

```bibtex
@inproceedings{schroeter2023deepfilternet3,
  title={DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement},
  author={Schroeter, Hendrik and Maier, Andreas and Escalante-B, Alberto N and Rosenkranz, Tobias},
  booktitle={Interspeech},
  year={2023}
}
```