Update README.md

README.md

@@ -1,3 +1,120 @@
----
-license: apache-2.0
----
+```yaml
+---
+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: `enhance.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_zoo/tflite/<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 enhance.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