DPDFNet

Real-time speech enhancement for recordings, live streams, and edge devices.
Pretrained 8, 16, and 48 kHz models for the CLI, Python API, and stateful streaming.

     

Live demo  ·  Audio examples  ·  Pretrained models  ·  Paper

Model Profile

8 kHz models

Model	Params [M]	MACs [G]	TFLite Size [MB]	ONNX Size [MB]
dpdfnet2_8khz	2.51	1.29	10.5	9.7
dpdfnet8_8khz	3.56	3.99	16.5	13.8

16 kHz models

Model	Params [M]	MACs [G]	TFLite Size [MB]	ONNX Size [MB]
baseline	2.31	0.36	8.5	8.3
dpdfnet2	2.49	1.35	10.7	9.7
dpdfnet4	2.84	2.36	12.9	11.1
dpdfnet8	3.54	4.37	17.2	13.9

48 kHz models

Model	Params [M]	MACs [G]	TFLite Size [MB]	ONNX Size [MB]
dpdfnet2_48khz_hr	2.58	2.42	11.6	10.0
dpdfnet8_48khz_hr	3.63	7.17	18.7	14.2

Install the PyPI Package

For CPU-only ONNX inference using the packaged CLI and Python API:

pip install dpdfnet

CLI Example

# Enhance one file
dpdfnet enhance noisy.wav enhanced.wav --model dpdfnet4 --attn-limit-db 12

# Enhance a directory (uses all CPU cores by default)
dpdfnet enhance-dir ./noisy_wavs ./enhanced_wavs --model dpdfnet2 --attn-limit-db 12

# Enhance a directory with a fixed worker count
dpdfnet enhance-dir ./noisy_wavs ./enhanced_wavs --model dpdfnet2 --workers 4 --attn-limit-db 12

# Download models
dpdfnet download
dpdfnet download dpdfnet8
dpdfnet download dpdfnet2_8khz
dpdfnet download dpdfnet4 --force

Python API Example

import soundfile as sf
import dpdfnet

# In-memory enhancement:
audio, sr = sf.read("noisy.wav")
enhanced = dpdfnet.enhance(audio, sample_rate=sr, model="dpdfnet4", attn_limit_db=12)
sf.write("enhanced.wav", enhanced, sr)

# Enhance one file:
out_path = dpdfnet.enhance_file("noisy.wav", model="dpdfnet2", attn_limit_db=12)
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
dpdfnet.download("dpdfnet2_8khz")

Streaming (Real-Time) API

Install sounddevice (not included in dpdfnet dependencies):

pip install sounddevice

StreamEnhancer processes audio chunk-by-chunk, preserving RNN state across calls. Any chunk size works; enhanced samples are returned as soon as enough data has accumulated for the first model frame (20 ms).

import numpy as np
import sounddevice as sd
import dpdfnet

INPUT_SR   = 48000
# Use one model hop (10 ms) as the block size so process() returns
# exactly one hop's worth of enhanced audio on every callback.
BLOCK_SIZE = int(INPUT_SR * 0.010)   # 480 samples at 48 kHz

enhancer = dpdfnet.StreamEnhancer(model="dpdfnet2_48khz_hr")

def callback(indata, outdata, frames, time, status):
    mono_in = indata[:, 0] if indata.ndim > 1 else indata.ravel()
    enhanced = enhancer.process(mono_in, sample_rate=INPUT_SR)
    n = min(len(enhanced), frames)
    outdata[:n, 0] = enhanced[:n]
    if n < frames:
        outdata[n:] = 0.0   # silence while the first window accumulates

with sd.Stream(
    samplerate=INPUT_SR,
    blocksize=BLOCK_SIZE,
    channels=1,
    dtype="float32",
    callback=callback,
):
    print("Enhancing microphone input - press Ctrl+C to stop")
    try:
        while True:
            sd.sleep(100)
    except KeyboardInterrupt:
        pass

# Optional: drain the final partial window at the end of a recording
tail = enhancer.flush()

Notes:

Latency - the first enhanced output arrives after one full model window (~20 ms) has been buffered. All subsequent blocks are returned with ~10 ms additional delay.
Sample rate - StreamEnhancer resamples internally. Pass your device's native rate as sample_rate; the return value is at the same rate.
Block size - using BLOCK_SIZE = int(SR * 0.010) (one model hop) gives one enhanced block per callback. Other sizes also work but may produce empty returns while the buffer fills.
Multiple streams - create a separate StreamEnhancer per stream. Call enhancer.reset() between independent audio segments to clear RNN state.

Run From Source

1) Install dependencies

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2) Download models

Model files are not bundled in this repository.
Download PyTorch checkpoints, TFLite, and ONNX models from Hugging Face:

pip install -U "huggingface_hub[cli]"

# create target dirs
mkdir -p model_zoo/{checkpoints,onnx,tflite}

# PyTorch checkpoints (HF path: checkpoints/* -> local: model_zoo/checkpoints/*)
hf download Ceva-IP/DPDFNet \
  --include "checkpoints/*.pth" \
  --local-dir model_zoo \

# ONNX models (HF path: onnx/* -> local: model_zoo/onnx/*)
hf download Ceva-IP/DPDFNet \
  --include "onnx/*.onnx" \
  --local-dir model_zoo \

# TFLite models (HF path: *.tflite at repo root -> local: model_zoo/tflite/*)
hf download Ceva-IP/DPDFNet \
  --include "*.tflite" \
  --local-dir model_zoo/tflite \

3) Run offline enhancement

Put one or more *.wav files in ./noisy_wavs, then choose one:

Option A: TFLite

python -m tflite_model.infer_dpdfnet_tflite \
	--noisy_dir ./noisy_wavs \
	--enhanced_dir ./enhanced_wavs \
	--model_name dpdfnet4 \
	--workers 5 \
	--attn-limit-db 12

Option B: ONNX

python -m onnx_model.infer_dpdfnet_onnx \
  	--noisy_dir ./noisy_wavs \
	--enhanced_dir ./enhanced_wavs \
	--model_name dpdfnet4 \
	--workers 5 \
	--attn-limit-db 12

To export ONNX models from checkpoints, use the exporter that matches the sample-rate family. Set --dprnn-num-blocks to match the checkpoint variant; for 8 kHz and 48 kHz HR exports, this also determines the ONNX metadata profile:

# 8 kHz
python -m onnx_model.export_dpdfnet_8khz_to_onnx \
	--checkpoint model_zoo/checkpoints/dpdfnet2_8khz.pth \
	--output model_zoo/onnx/dpdfnet2_8khz.onnx \
	--dprnn-num-blocks 2

# 16 kHz
python -m onnx_model.export_dpdfnet_to_onnx \
	--checkpoint model_zoo/checkpoints/dpdfnet4.pth \
	--output model_zoo/onnx/dpdfnet4.onnx \
	--dprnn-num-blocks 4

# 48 kHz HR
python -m onnx_model.export_dpdfnet_48khz_hr_to_onnx \
	--checkpoint model_zoo/checkpoints/dpdfnet8_48khz_hr.pth \
	--output model_zoo/onnx/dpdfnet8_48khz_hr.onnx \
	--dprnn-num-blocks 8

Enhanced files are written as:

<original_stem>_<model_name>.wav

Audio Samples & Demo

• Project page with examples: https://ceva-ip.github.io/DPDFNet/
• Gradio application: https://huggingface.co/spaces/Ceva-IP/DPDFNetDemo
• Hugging Face model hub: https://huggingface.co/Ceva-IP/DPDFNet
• Evaluation dataset used in the paper: https://huggingface.co/datasets/Ceva-IP/DPDFNet_EvalSet

Real-Time Demo

Run:

python -m real_time_demo

How it works:

• Captures microphone audio in streaming hops.
• Enhances each hop frame-by-frame with ONNX.
• Displays live noisy vs enhanced spectrograms.
• Allows you to control the noise‑reduction level during playback: 0 for the raw stream and 1 for the fully enhanced stream.
• Enables the use of AGC during playback.

To change model, edit MODEL_NAME near the top of real_time_demo.py.

Troubleshooting / FAQ

Q: Model files are missing (TFLite / ONNX / checkpoints)

• Run the Hugging Face download commands from the Run From Source section.
• Confirm files are in:
  • model_zoo/tflite/
  • model_zoo/onnx/
  • model_zoo/checkpoints/

Q: No .wav files found

• Both offline scripts scan only the exact folder given by --noisy_dir (non-recursive).
• Ensure input files use .wav extension.

Q: Real-time demo has audio device errors

• Check microphone permissions and default input/output device settings.
• Install host audio dependencies for sounddevice (PortAudio packages on your OS).

Q: Real-time GUI does not open

• Ensure Qt dependencies from requirements.txt installed successfully.
• On headless servers, run offline enhancement instead.

Q: I get import/module errors when running commands

• Run from repo root and use module form exactly as documented (python -m ...).
• Activate your virtual environment before running commands.

Q: CPU is too slow for my target

• Try smaller models (baseline, dpdfnet2).
• Benchmark ONNX runtime using python -m onnx_model.infer_dpdfnet_onnx ... and compare RTF.

Evaluation Metrics

To compute intrusive and non-intrusive metrics on our DPDFNet EvalSet, we use the tools listed below. For aggregate quality reporting, we rely on PRISM, the scale‑normalized composite metric introduced in the DPDFNet paper.

Intrusive metrics: PESQ, STOI, SI-SNR

We provide a dedicated script, pesq_stoi_sisnr_calc.py, which computes PESQ, STOI, and SI-SNR for paired reference and enhanced audio. The script includes a built-in auto-alignment step that corrects small start-time offsets and drift between the reference and the enhanced signals before scoring, to ensure fair comparisons.

Non-intrusive metrics

• DNSMOS (P.835 & P.808) - We use the official DNSMOS local inference script from the DNS Challenge repository: dnsmos_local.py. Please follow their installation and model download instructions in that project before running.
• NISQA v2 - We use the official NISQA project: https://github.com/gabrielmittag/NISQA. Refer to their README for environment setup, pretrained model weights, and inference commands (e.g., running nisqa_predict.py on a folder of WAVs).

Built with DPDFNet

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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},
}

License

Apache License 2.0. See LICENSE.