Models & Training

RFWhisper ships two model families out of the box and makes it easy to add your own.

Model	Flavor	Typical use	Size	CPU RTF (M1)	CPU RTF (RPi 5)
DeepFilterNet3 (ham-tuned)	FP32	Default on laptops/desktops	~3.1 MB	0.12	0.41
DeepFilterNet3 (INT8 QDQ)	INT8	Laptops + RPi 5	~0.9 MB	0.07	0.22
RNNoise-ham (retuned)	FP32	Pi 4 / Zero 2 W / anywhere CPU is tight	~180 KB	0.03	0.09
Your fine-tune (v0.5)	FP32 / INT8	Tuned to your QTH's noise	any	varies	varies

Every model ships with

• SHA-256 pinned in source (rfwhisper/models/registry.yaml)
• A model card in docs/models/model-cards (architecture, training data, license, known failure modes, maintainer)
• PyTorch ↔ ONNX parity verification (RMS diff ≤ 1e-3, max ≤ 1e-2)
• CW + FT8 regression test results on the reference eval set
• Latency p50 / p99 on reference hardware

Pick a model

# Default — pipeline will choose based on your hardware
rfwhisper denoise-live --model auto

# Explicit
rfwhisper denoise-live --model deepfilternet3 --model-variant int8
rfwhisper denoise-live --model rnnoise-ham
rfwhisper denoise-live --model my-qth-tuned-v1     # your fine-tune

Model registry

Models are registered in rfwhisper/models/registry.yaml:

deepfilternet3:
  variants:
    fp32:
      url: https://dl.rfwhisper.org/models/dfn3-ham-fp32-v1.onnx
      sha256: 5c6e...d14b
      opset: 17
      native_rate_hz: 48000
      frame_ms: 10
    int8:
      url: https://dl.rfwhisper.org/models/dfn3-ham-int8-v1.onnx
      sha256: a19f...9c02
      opset: 17
      native_rate_hz: 48000
      frame_ms: 10
  card: docs/models/deepfilternet3-ham.md
  license: MIT

rfwhisper models fetch downloads, verifies, and caches these under ~/.cache/rfwhisper/models/.

The non-regression gates (every model must pass)

1. CW keying transients — RMS within ±1 dB of raw on every transient.
2. FT8 decodes — denoised decodes ≥ raw, zero false decodes.
3. No-op sanity — clean in, clean out. PESQ drop ≤ 0.3, STOI drop ≤ 0.02.

A model that fails any of these is not merged, even if it scores higher on SNR gain.

Next

• Training — build a dataset + train from scratch
• Fine-tuning — adapt a stock model to your shack in ~30 minutes
• Model Cards — what's in each model, who maintains it, and how it was evaluated