dpdfnet 0.3.0

CPU-only ONNX inference package for DPDFNet speech enhancement.

dpdfnet

CPU-only ONNX inference package for DPDFNet speech enhancement.

Installation

pip install dpdfnet

Requirements

• Python >=3.11
• OS support for soundfile / libsndfile

Runtime dependencies are installed automatically:

• numpy
• librosa
• soundfile
• onnxruntime
• filelock
• tqdm

CLI

Show help:

dpdfnet --help

Commands:

1. dpdfnet models

• List supported models and local availability.

2. dpdfnet enhance <input.wav> <output.wav> [--model <name>] [-v|--verbose]

• Enhance one WAV file.

3. dpdfnet enhance-dir <input_dir> <output_dir> [--model <name>] [-v|--verbose]

• Enhance all .wav files in a directory (non-recursive).

4. dpdfnet download [model] [--force|--refresh] [-q|--quiet | -v|--verbose]

• Download all models when model is omitted, or one model when provided.

CLI examples:

# Enhance one file
dpdfnet enhance noisy.wav enhanced.wav --model dpdfnet4

# Enhance a directory
dpdfnet enhance-dir ./noisy_wavs ./enhanced_wavs --model dpdfnet2

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

Python API

Top-level exports:

• dpdfnet.enhance
• dpdfnet.enhance_file
• dpdfnet.available_models
• dpdfnet.download

In-memory enhancement:

import soundfile as sf
import dpdfnet

audio, sr = sf.read("noisy.wav")
enhanced = dpdfnet.enhance(audio, sample_rate=sr, model="dpdfnet4")
sf.write("enhanced.wav", enhanced, sr)

Enhance one file:

import dpdfnet

out_path = dpdfnet.enhance_file("noisy.wav", model="dpdfnet2")
print(out_path)

Model listing:

import dpdfnet

for row in dpdfnet.available_models():
    print(row["name"], row["ready"], row["cached"])

Download models via API:

import dpdfnet

dpdfnet.download()
dpdfnet.download("dpdfnet4")

Links

• Homepage: https://github.com/ceva-ip/DPDFNet
• Issues: https://github.com/ceva-ip/DPDFNet/issues