audiosronnx-dpdfnet

ONNX weights for the dpdfnet denoise engine in audiosronnx — a mirror of Ceva-IP/DPDFNet (Apache-2.0), pinned so the library can resolve a fixed revision.

DPDFNet (Dual-Path RNN-based DeepFilterNet) is a streaming speech denoiser built on DeepFilterNet2. Each graph is stateful and consumes one STFT frame at a time:

spec[1, 1, F, 2], state[S]  ->  spec_e[1, 1, F, 2], state[S]

The Vorbis-windowed STFT/ISTFT runs in numpy inside audiosronnx, so inference is onnxruntime-only — no torch, and no libdf (unlike the DeepFilterNet3 engine).

File	Rate	Size
baseline.onnx	16 kHz	8.7 MB
dpdfnet2.onnx	16 kHz	10.2 MB
dpdfnet4.onnx	16 kHz	11.6 MB
dpdfnet8.onnx	16 kHz	14.5 MB
dpdfnet2_8khz.onnx	8 kHz	10.2 MB
dpdfnet8_8khz.onnx	8 kHz	14.6 MB
dpdfnet2_48khz_hr.onnx	48 kHz	10.5 MB
dpdfnet8_48khz_hr.onnx	48 kHz	14.9 MB

The adapter reproduces the upstream reference implementation to max abs err 1.7e-8 (correlation 1.000000) on identical input.

Usage

from audiosronnx import load_denoise

dn = load_denoise("dpdfnet")                          # dpdfnet2_48khz by default
clean, rate = dn.denoise("noisy_call.wav")

dn = load_denoise("dpdfnet", model="dpdfnet8")        # 16 kHz, highest quality
dn = load_denoise("dpdfnet", attn_limit_db=12)        # keep a natural noise floor

License

Apache-2.0, inherited from the upstream DPDFNet release.