aufklarer / DeepFilterNet3-MLX

DeepFilterNet3 — MLX

Real-time speech enhancement for Apple Silicon. Removes background noise from speech audio. FP32 MLX weights converted from the official DeepFilterNet3 checkpoint, with BatchNorm fused into the convolutions.

• 2.1M params , fp32 safetensors, 8.1 MB
• 48 kHz native, 10 ms frames, 2-frame (20 ms) lookahead
• Network-only export: STFT, ERB feature extraction, and deep-filter application live in the runtime ( auxiliary.npz ships the exact DSP constants)

Model

Files

Quality

30 VoiceBank-DEMAND test clips (16 kHz mirror, resampled to 48 kHz for the model); the neural network forward runs on MLX while STFT / ERB / deep-filter post-processing stay in the upstream PyTorch pipeline — the same methodology used for the CoreML variant .

Network outputs match PyTorch within 9e-7 on the MLX CPU stream — numerically exact for fp32. The GPU stream uses Metal fast-math transcendentals (max output delta ~4e-3) with no measurable metric impact.

Latency (Apple M5 Pro, network forward only, Python MLX)

Usage

import sys

import mlx.core as mx
from huggingface_hub import snapshot_download

model_dir = snapshot_download("aufklarer/DeepFilterNet3-MLX")
sys.path.append(model_dir)
from dfn3_mlx import DFN3MLX

model = DFN3MLX(model_dir)

# Normalized features from your DSP front-end (constants in auxiliary.npz):
feat_erb = mx.zeros((1, 100, 32, 1))   # ERB features, dB-scaled + mean-normalized
feat_spec = mx.zeros((1, 100, 96, 2))  # complex spectrum (real, imag), unit-normalized

erb_mask, df_coefs, lsnr = model(feat_erb, feat_spec)

Full audio-in/audio-out enhancement additionally needs the DeepFilterNet DSP path (STFT → features → ERB mask + deep filtering → iSTFT), documented in the upstream repository .

On Apple devices, speech-swift runs this model directly via its MLX engine (or use the CoreML variant for the Neural Engine):

import SpeechEnhancement

let enhancer = try await SpeechEnhancer.fromPretrained(engine: .mlx)
let clean = try enhancer.enhance(audio: noisyAudio, sampleRate: 48000)

CLI:

swift run speech denoise noisy.wav --engine mlx

Tensor contract

Channels-last [B, T, F, C] — time is the conv H axis, frequency the W axis. The 2-frame lookahead shift is applied to the inputs inside the model.

Weight conventions

• Conv2d : [O, kH, kW, I/groups] (PyTorch [O, I/g, kH, kW] transposed); causal time padding, symmetric frequency padding
• Depthwise ConvTranspose2d ( erb_dec.convt{1,2}.dwt ): [C, kH, kW, 1] , true transposed-conv kernels (not pre-flipped); k=(1,3), freq stride 2, padding 1, output padding 1
• GRU ( mlx.nn.GRU convention, gate order r/z/n): Wx = weight_ih , Wh = weight_hh , b = bias_ih + [bias_hh_r; bias_hh_z; 0] , bhn = bias_hh_n ; run with an explicit zero initial hidden state
• Grouped linear : [groups, in/groups, out/groups] , applied as einsum("btgi,gih->btgh") then flattened
• BatchNorm is fused into the preceding conv; the unused df_fc_a head is dropped

Source

• Base model: Rikorose/DeepFilterNet v0.5.6 checkpoint (Apache-2.0 / MIT)
• Paper: DeepFilterNet3

License

• Model weights: Apache-2.0 / MIT dual license
• MLX conversion: Apache-2.0

Links

• speech-swift — Apple SDK
• Docs — install and CLI docs
• soniqo.audio — website
• blog — blog