soniqo/DeepFilterNet3-ONNX

DeepFilterNet3 ONNX

Verified neural-network export of the official DeepFilterNet3 v0.5.6 checkpoint for native 48 kHz speech enhancement.

This is the neural graph only. It expects normalized spectral features and returns an ERB mask plus complex deep-filter coefficients. A compatible STFT, feature-normalization, filtering, and synthesis implementation is required.

Files

File	Description
deepfilter.onnx	FP32 ONNX graph with a dynamic frame axis
deepfilter-auxiliary.bin	Canonical ERB matrices and Vorbis window for legacy consumers
config.json	Model and DSP configuration
export_manifest.json	Source checksum, artifact checksums, and ONNX parity results

Tensor contract

Direction	Name	Shape	Type
input	feat_erb	[1, 1, T, 32]	float32
input	feat_spec	[1, 2, T, 96]	float32
output	erb_mask	[1, 1, T, 32]	float32
output	df_coefs	[1, 5, T, 96, 2]	float32

The feat_spec channels are real then imaginary. The coefficient axes are batch, filter order, frame, frequency, then real/imaginary.

Required DSP contract

• sample rate: 48,000 Hz
• STFT: 960-point real FFT, 480-sample hop, Vorbis window
• spectrum: 481 bins; deep filtering: first 96 bins
• ERB bank: 32 disjoint bands with at least 2 FFT bins per band
• normalization: tau=1.0, rounded alpha 0.99; ERB mean state linearly initialized from -60 dB to -90 dB and complex unit state from 0.001 to 0.0001
• deep filter: order 5, lookahead 2, applied from an immutable copy of the noisy spectrum before fusion with the ERB-masked spectrum
• batch/offline parity: append one 960-sample analysis tail and remove the 480-sample STFT delay after overlap-add synthesis

deepfilter-auxiliary.bin is little-endian float32 data containing, in order, the [481,32] forward ERB matrix, [32,481] inverse ERB matrix, and [960] Vorbis window. New integrations can generate these deterministic constants.

Verification

The export script checks the graph with ONNX, then compares ONNX Runtime CPU outputs with PyTorch for several frame lengths. Exact errors and SHA-256 hashes are recorded in export_manifest.json.

End-to-end validation used 20 VoiceBank-DEMAND clips (101.51 seconds total):

Runtime	PESQ	STOI	SI-SDR
official DeepFilterNet v0.5.6	2.9585	0.96064	18.228 dB
speech-core with this graph	2.9602	0.96064	18.225 dB

The speech-core output matched the official reference at 54.85 dB mean SI-SDR with 0.000140 mean waveform RMSE. These values validate the tested native DSP integration; other implementations must follow the contract above.

Attribution and license

DeepFilterNet is by Hendrik Schröter and contributors and is dual-licensed under Apache-2.0 or MIT. This repository publishes the converted model under the MIT option. See the upstream project and papers for architecture and training details.