DeepFilterNet3 for Core ML (streaming, 100 frames, FP16)
A Core ML conversion of DeepFilterNet3 by Hendrik Schröter, used for noise reduction in Clarity Sound Studio, a free, open-source macOS app.
Only the network is converted. The DSP around it (STFT, ERB and complex features, the mask, the deep filter and the inverse STFT) runs in the app. dfn3_dsp.json records that contract: window, ERB band widths, normalisation constants and the coefficient layout. The same JSON is in the model's user_defined_metadata["dsp"].
| File | DeepFilterNet3-Stream-T100-fp16.mlpackage (ML Program, FP16, macOS 14+) |
| Upstream | DeepFilterNet3 weights at commit d375b2d (dfn3-epoch120-best) |
| Conversion | convert.py --frames 100 --precision fp16 --gru unrolled: stateful in chunks of 100 frames (10 ms hop at 48 kHz), the GRU and convolution state passed in and out as tensors |
| Inputs | feat_erb, feat_spec and six state tensors (zero at the start) |
| Outputs | mask, coefs and the next state |
The weights are unchanged apart from the conversion to FP16.
Licence
DeepFilterNet is dual-licensed under MIT or Apache-2.0. This conversion is distributed under the MIT licence; see LICENSE.
Copyright (c) 2021 Hendrik Schröter.
H. Schröter, T. Rosenkranz, A. N. Escalante-B. and A. Maier, "DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement", Interspeech 2023.
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