dpdfnet 0.4.0

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

Supported Audio Formats

The following input formats are supported out of the box (via soundfile/libsndfile):

Format	Extensions
WAV	.wav
FLAC	.flac
Ogg Vorbis	.ogg
AIFF	.aiff, .aif
AU/SND	.au, .snd

MP3 and other compressed formats require the optional pydub dependency and ffmpeg on your PATH:

pip install 'dpdfnet[mp3]'
# also install ffmpeg, e.g.:
#   Ubuntu/Debian:  sudo apt install ffmpeg
#   macOS:          brew install ffmpeg
#   Windows:        https://ffmpeg.org/download.html

Once installed, these additional formats are supported:

Format	Extensions
MP3	.mp3
AAC / M4A	.aac, .m4a
WMA	.wma
Opus	.opus

Output is always written as PCM16 .wav regardless of the input format.

CLI

Show help:

dpdfnet --help

Commands:

1. dpdfnet models

• List supported models and local availability.

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

• Enhance one audio file (any supported format; output is always .wav).

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

• Enhance all supported audio files in a directory (non-recursive).
• Files are processed concurrently; --workers sets the thread count (default: CPU count).

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 (uses all CPU cores by default)
dpdfnet enhance-dir ./noisy_wavs ./enhanced_wavs --model dpdfnet2

# Enhance a directory with a fixed worker count
dpdfnet enhance-dir ./noisy_wavs ./enhanced_wavs --workers 4

# 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")

Real-time Microphone Enhancement

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.

Links

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