README.md

metadata

license: mit
library_name: mlx
tags:
  - mlx
  - audio
  - speech-enhancement
  - noise-suppression
  - deepfilternet
  - apple-silicon
base_model: DeepFilterNet/DeepFilterNet3
pipeline_tag: audio-to-audio

DeepFilterNet3 — MLX

MLX-compatible weights for DeepFilterNet3, 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 on Apple Silicon.

Origin

• Original model: DeepFilterNet3 by Hendrik Schröter
• Paper: DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
• License: MIT (same as the original)
• Conversion: PyTorch → safetensors via the included convert_deepfilternet.py script

No fine-tuning or quantisation was applied — the weights are numerically identical to the original checkpoint.

Files

File	Description
config.json	Model architecture configuration
model.safetensors	Pre-converted weights (8.3 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
Parameters	~2M

Usage

Swift (mlx-audio-swift)

import MLXAudioSTS

let model = try await DeepFilterNetModel.fromPretrained("iky1e/DeepFilterNet3-MLX")
let enhanced = try model.enhance(audioArray)

Python (mlx-audio)

from mlx_audio.sts.models.deepfilternet import DeepFilterNetModel

model = DeepFilterNetModel.from_pretrained(version=3, model_dir="path/to/local/dir")
enhanced = model.enhance("noisy.wav")

Converting from PyTorch

To re-create this conversion from the original DeepFilterNet checkpoint:

python convert_deepfilternet.py \
  --input /path/to/DeepFilterNet3 \
  --output ./DeepFilterNet3-MLX \
  --name DeepFilterNet3

The input directory should contain a config.ini and a checkpoints/ folder from the original repo.

Citation

@inproceedings{schroeter2023deepfilternet3,
  title={DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement},
  author={Schr{\"o}ter, Hendrik and Rosenkranz, Tobias and Escalante-B., Alberto N. and Maier, Andreas},
  booktitle={INTERSPEECH},
  year={2023}
}