DPDFNet

DPDFNet is a family of causal, single‑channel speech enhancement models for real‑time noise suppression.
It builds on DeepFilterNet2 by adding Dual‑Path RNN (DPRNN) blocks in the encoder for stronger long‑range modeling while staying streaming‑friendly.

Links

• Project page (audio samples + architecture): https://ceva-ip.github.io/DPDFNet/
• Paper (arXiv): https://arxiv.org/abs/2512.16420
• Code (GitHub): https://github.com/ceva-ip/DPDFNet
• Demo Space: https://huggingface.co/spaces/Ceva-IP/DPDFNetDemo
• Evaluation set: https://huggingface.co/datasets/Ceva-IP/DPDFNet_EvalSet

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What’s in this repo

• TFLite: *.tflite (root)
• ONNX: onnx/*.onnx
• PyTorch checkpoints: checkpoints/*.pth

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Model variants

16 kHz models

Model	DPRNN blocks	Params (M)	MACs (G)
baseline	0	2.31	0.36
dpdfnet2	2	2.49	1.35
dpdfnet4	4	2.84	2.36
dpdfnet8	8	3.54	4.37

48 kHz fullband model

Model	DPRNN blocks	Params (M)	MACs (G)
dpdfnet2_48khz_hr	2	2.58	2.42

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Recommended inference (CPU-only, ONNX)

pip install dpdfnet

CLI

# Enhance one file
dpdfnet enhance noisy.wav enhanced.wav --model dpdfnet4

# Enhance a directory
dpdfnet enhance-dir ./noisy_wavs ./enhanced_wavs --model dpdfnet2

# Download models
dpdfnet download
dpdfnet download dpdfnet8
dpdfnet download dpdfnet4 --force

Python API

import soundfile as sf
import dpdfnet

# In-memory enhancement:
audio, sr = sf.read("noisy.wav")
enhanced = dpdfnet.enhance(audio, sample_rate=sr, model="dpdfnet4")
sf.write("enhanced.wav", enhanced, sr)

# Enhance one file:
out_path = dpdfnet.enhance_file("noisy.wav", model="dpdfnet2")
print(out_path)

# Model listing:
for row in dpdfnet.available_models():
    print(row["name"], row["ready"], row["cached"])

# Download models:
dpdfnet.download()				# All models
dpdfnet.download("dpdfnet4")	# Specific model

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Citation

@article{rika2025dpdfnet,
  title  = {DPDFNet: Boosting DeepFilterNet2 via Dual-Path RNN},
  author = {Rika, Daniel and Sapir, Nino and Gus, Ido},
  year   = {2025}
}

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License

Apache-2.0