---
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