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license: mit
library_name: mlx
tags:
- mlx
- audio
- speech-enhancement
- noise-suppression
- deepfilternet
- apple-silicon
base_model: DeepFilterNet/DeepFilterNet
pipeline_tag: audio-to-audio
---
# DeepFilterNet1 — MLX
MLX-compatible weights for [DeepFilterNet](https://github.com/Rikorose/DeepFilterNet), a real-time speech enhancement model that suppresses background noise from audio.
This is a direct conversion of the original PyTorch weights to `safetensors` format for use with [MLX](https://github.com/ml-explore/mlx) on Apple Silicon.
## Origin
- **Original model:** [DeepFilterNet](https://github.com/Rikorose/DeepFilterNet) by Hendrik Schroeter
- **Paper:** [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://arxiv.org/abs/2110.05588)
- **License:** MIT (same as the original)
- **Conversion:** PyTorch -> `safetensors` via the included `convert_deepfilternet.py` script
No fine-tuning or quantization was applied. Weights are converted directly from the original checkpoint.
## Files
| File | Description |
|---|---|
| `config.json` | Model architecture configuration |
| `model.safetensors` | Pre-converted weights (~7.2 MB, float32) |
| `convert_deepfilternet.py` | Conversion script (PyTorch -> MLX safetensors) |
## Model Details
| Parameter | Value |
|---|---|
| Sample rate | 48 kHz |
| FFT size | 960 |
| Hop size | 480 |
| ERB bands | 32 |
| DF bins | 96 |
| DF order | 5 |
| Embedding hidden dim | 512 |
## Usage
### Swift (mlx-audio-swift)
```swift
import MLXAudioSTS
let model = try await DeepFilterNetModel.fromPretrained("iky1e/DeepFilterNet1-MLX")
let enhanced = try model.enhance(audioArray)
```
### Python (mlx-audio)
```python
from mlx_audio.sts.models.deepfilternet import DeepFilterNetModel
model = DeepFilterNetModel.from_pretrained("iky1e/DeepFilterNet1-MLX")
enhanced = model.enhance("noisy.wav")
```
## Converting from PyTorch
```bash
python convert_deepfilternet.py \
--input /path/to/DeepFilterNet \
--output ./DeepFilterNet1-MLX \
--name DeepFilterNet
```
## Citation
```bibtex
@inproceedings{schroeter2022deepfilternet,
title={{DeepFilterNet}: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering},
author = {Schr{\"o}ter, Hendrik and Escalante-B., Alberto N. and Rosenkranz, Tobias and Maier, Andreas},
booktitle={ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2022},
organization={IEEE}
}
```
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