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---
license: apache-2.0
pipeline_tag: audio-to-audio
tags:
- speech_enhancement
- noise_suppression
- real_time
---
# 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`
---
## 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.
---
## 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 |
---
## 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
---
## 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:
```bash
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:
```bash
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`.
---
## Training Data
The models were trained using a mixture of public speech and noise datasets, including DNS4 (downsampled), MLS, MUSAN, and FSD50K.
---
## Citation
If you use these models, please cite:
```bibtex
@article{rika2025dpdfnet,
title = {DPDFNet: Boosting DeepFilterNet2 via Dual-Path RNN},
author = {Rika, Daniel and Sapir, Nino and Gus, Ido},
year = {2025}
}
```
---
## License
Apache-2.0
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