DPDFNet: Boosting DeepFilterNet2 via Dual-Path RNN

DPDFNet is a family of causal, single-channel speech enhancement models for real-time noise suppression in challenging everyday environments. It extends the DeepFilterNet2 enhancement framework by inserting Dual-Path RNN (DPRNN) blocks into the encoder, strengthening long-range temporal and cross-band modeling while preserving a compact, streaming-friendly design.

This repository provides four TensorFlow Lite (TFLite) models optimized for mobile and edge deployment:

• baseline.tflite
• dpdfnet2.tflite
• dpdfnet4.tflite
• dpdfnet8.tflite

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Key Features

• Causal and low-latency: Designed for streaming use cases such as telephony, conferencing, and embedded devices.
• Dual-Path RNN integration: Improves temporal context and frequency-domain interactions for more robust enhancement in difficult noise conditions.
• Scalable family: Choose baseline or dpdfnet2/4/8 to balance quality vs. compute.
• Edge deployment focus: Demonstrated on Ceva NeuPro Nano NPUs in the accompanying work.

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Model Variants and Footprint

Model	Params [M]	MACs [G]	TFLite Size [MB]
Baseline	2.31	0.36	8.5
DPDFNet-2	2.49	1.35	10.7
DPDFNet-4	2.84	2.36	12.9
DPDFNet-8	3.54	4.37	17.2

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Intended Use

Primary task: Real-time, single-channel speech enhancement (noise suppression).

Deployment targets: Mobile devices, embedded NPUs, and edge platforms.

Input and Output:

• Input: 16 kHz mono noisy speech waveform
• Output: 16 kHz mono enhanced speech waveform

Typical applications:

• Voice calls and VoIP
• Video conferencing
• Always-on voice interfaces
• Wearables, earbuds, and embedded audio devices

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Inference

This repo includes a reference script for running the TFLite models on WAV files using streaming-style, frame-by-frame inference: run_tflite.py.

Setup

Install dependencies:

pip install numpy soundfile librosa tqdm
pip install tflite-runtime

Model placement

By default, the script loads models from:

• ./<model_name>.tflite

Create the folder and place the .tflite files there (or edit TFLITE_DIR in the script to match your layout).

Run enhancement on a folder of WAVs

The script processes *.wav files non-recursively and writes enhanced outputs as 16-bit PCM WAVs:

python run_tflite.py --noisy_dir /path/to/noisy_wavs --enhanced_dir /path/to/out --model_name dpdfnet8

Available --model_name options: baseline, dpdfnet2, dpdfnet4, dpdfnet8.

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Training Data

The models were trained using a mixture of public speech and noise datasets, including DNS4 (downsampled), MLS, MUSAN, and FSD50K.

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Citation

If you use these models, please cite:

@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