Roadmap

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"If you can't demo it to another ham and have them say 'that's clearly better,' it doesn't ship."

This roadmap is phased, explicit, and testable. Every milestone lists:

Scope — what's in, what's out
Deliverables — concrete artifacts, files, binaries, docs
Acceptance criteria — measurable pass/fail gates (automated where possible)
Testable success examples — real-world demos a human operator can verify
Stretch goals — nice-to-haves if we have bandwidth
Exit criteria — the one-line "we can tag this release when..." check

If a proposed PR does not move at least one of these forward, it probably belongs on a branch, not in main.

Guiding Principles

Ship something useful every release. Even v0.1 should be genuinely better for hams than nothing.
Testable > theoretical. If there is no acceptance criterion, there is no feature.
Preserve the signal. We will refuse to ship any denoiser that measurably hurts CW transients or FT8/FT4 decode rates.
Local-first, always. Zero cloud dependencies in core. No telemetry without explicit opt-in.
Cross-platform from day one. Linux, macOS, Windows, Raspberry Pi 5.
Latency is a feature. v0.1 < 100 ms, v0.3 target < 50 ms, v1.0 documented budget per mode.
Community ≥ code. Docs, samples, and onboarding are first-class deliverables, not afterthoughts.

Latency Budget (reference)

Stage	v0.1 target	v0.3 target	Notes
Audio capture (frame)	10–20 ms	5–10 ms	PortAudio / ALSA / JACK / WASAPI
Pre-emphasis + feature extraction	≤ 5 ms	≤ 2 ms	VOLK + liquid-dsp SIMD
ONNX inference (DFN3)	≤ 30 ms	≤ 15 ms	XNNPACK/CoreML/DirectML; INT8 stretch
Post-processing + overlap-add	≤ 5 ms	≤ 2 ms	—
Output buffering	10–30 ms	5–15 ms	—
End-to-end (p99)	< 100 ms	< 50 ms	Measured on M1 / i5-8xxx / RPi 5

RPi Zero 2 W ships with RNNoise (not DFN3) and has a separate, looser budget.

v0.1 — Audio-Only Denoiser → Virtual Cable

Theme: Prove the core value proposition with the smallest possible surface area. One executable, one model, one virtual cable, demonstrably better copy.

Scope

In:

Offline WAV denoising (CLI)
Real-time audio-in → audio-out (virtual cable friendly)
DeepFilterNet3 primary model (ONNX, CPU)
RNNoise fallback model (ONNX, CPU)
Minimal Tk/Qt GUI: A/B toggle, device picker, record before+after, live telemetry
Cross-platform: Linux / macOS / Windows / Raspberry Pi 5

Explicitly out:

Any SDR / RF / IQ input (that's v0.2)
GNU Radio flowgraph integration (v0.2)
Mode profiles, adaptive notch (v0.3)
Spectrogram UI (v0.4)
Fine-tuning tooling (v0.5)

Deliverables

rfwhisper Python package with pyproject.toml (installable via pip install -e .)
rfwhisper denoise — offline WAV in → WAV out + JSON report
rfwhisper denoise-live — real-time audio device → audio device
rfwhisper audio list — enumerate input/output devices
rfwhisper gui — minimal cross-platform GUI (PySide6 or Tk)
rfwhisper models fetch — downloads pre-converted DFN3 + RNNoise ONNX
models/deepfilternet3/model.onnx (or hosted with SHA-256 pinned in repo)
models/rnnoise/model.onnx (or hosted with SHA-256 pinned)
tests/audio/ with acceptance harness (see below)
docs/quickstart.md, docs/virtual-cable-setup.md (Win/Mac/Linux)
At least 5 seed samples in samples/ covering SSB, CW, FT8, VHF FM, powerline buzz

Acceptance Criteria (Pass/Fail)

#	Criterion	Measurement	Threshold
A1	Effective SNR gain on ham speech mix	Matched-filter correlation vs clean reference on `tests/audio/ssb_mix_*.wav`	≥ +3 dB avg, ≥ +6 dB on powerline-dominant clips
A2	No FT8 decode regressions	WSJT-X decoder run on raw vs denoised 15-min segment	Denoised decodes ≥ raw decodes, zero false decodes
A3	No CW transient damage	RMS energy in keying-onset window (first 5 ms of dit)	Within ±1 dB of raw
A4	End-to-end latency (p99)	`tests/audio/latency_probe.py` with impulse train	< 100 ms on i5-8xxx / M1 / RPi 5
A5	Real-time factor (RTF)	DFN3 CPU-only on target hardware	RTF < 0.5 (headroom for other tasks)
A6	No-op sanity (clean in → clean out)	PESQ / STOI on clean speech	PESQ drop ≤ 0.3, STOI drop ≤ 0.02
A7	Cross-platform install	CI matrix build + smoke test	Green on ubuntu-22.04, macos-13, windows-2022
A8	Virtual cable routing docs	Manual verification checklist	A beginner can get audio routed to WSJT-X in ≤ 10 minutes

All CI-enforceable criteria run in [.github/workflows/basic-ci.yml](https://github.com/jakenherman/rfwhisper/blob/main/.github/workflows/basic-ci.yml).

Testable Success Examples

"Weak 40m DX" demo — A 60-second clip of an S3 DX station under S7 powerline buzz is processed and played blind for 5 volunteer hams. ≥ 4/5 prefer the denoised version AND copy ≥ 1 extra word/sentence on average.
"FT8 marginal decode" demo — A 15-minute FT8 cycle with known weak stations, replayed through RFWhisper → virtual cable → WSJT-X, recovers at least as many stations as raw, with zero false decodes. Ideal: +1 to +3 marginals/cycle.
"CW under QRN" demo — 25 WPM CW with atmospheric crashes. fldigi copy accuracy does not drop; operator reports crashes reduced ≥ 6 dB audibly.
"POTA in a townhouse" demo — Field recording from a noisy QTH (solar inverter + neighbor VDSL). Operator reports "I can actually work this band now" in a recorded testimonial.
Latency probe — Impulse-train round trip < 100 ms p99 on reference hardware. Logged in CI.

Stretch Goals

INT8 quantized DFN3 variant for RPi 4 support
CoreML / DirectML / CUDA execution provider auto-select
Push-to-toggle keyboard shortcut in GUI
Waveform-only (no spectrogram) before/after viewer
One-liner install scripts (install.sh, install.ps1)

Exit Criteria — v0.1.0

Ship gate

All of A1–A8 pass in CI on the reference hardware matrix, at least 3 of the 5 testable-success demos are recorded and linked from the release notes, and the quickstart doc has been validated end-to-end by at least one non-author contributor on each OS.

v0.2 — GNU Radio + SoapySDR Pipeline

Theme: Take the audio MVP and wire it into a real SDR flowgraph. Live RF → denoised audio, on-air.

Scope

In:

GNU Radio 3.10.x flowgraph: SoapySDR source → demod (SSB/AM/FM/NBFM) → resample → gr-dnn ONNX denoiser block → audio sink
Pre-built flowgraphs for RTL-SDR, Airspy HF+, ADALM-Pluto, SDRplay, HackRF
rfwhisper flowgraph run <name> launcher
Working on at least 3 real SDRs with on-air QSOs documented

Out: Mode profiles (v0.3), UI overlays (v0.4), training tools (v0.5)

Deliverables

flowgraphs/rtl_ssb_hf.grc (and generated .py)
flowgraphs/airspy_hfplus_ssb.grc
flowgraphs/pluto_vhf_fm.grc
flowgraphs/sdrplay_ssb.grc
flowgraphs/hackrf_wbfm.grc
gr-rfwhisper/ — our own gr-dnn wrapper block with model-swap support, or a documented recipe for using stock gr-dnn
docs/gnuradio-install.md (per-OS)
docs/soapy-hardware-matrix.md with tested SDR list
On-air demo videos / recordings in release notes

Acceptance Criteria

#	Criterion	Threshold
B1	Live flowgraph runs end-to-end on ≥ 3 SDRs	RTL-SDR v4, Airspy HF+, 1 of {Pluto, SDRplay, HackRF}
B2	End-to-end latency (antenna → speaker)	< 250 ms p99 on reference laptop (includes radio DSP)
B3	CPU at 48 kHz audio + 384 ksps IQ	< 60 % of one core on i5-8xxx
B4	Flowgraph restart stability	1 hr soak without dropouts; 100 stop/start cycles without leaks
B5	Model hot-swap	Switching DFN3 ↔ RNNoise at runtime works without restart
B6	Retains all v0.1 acceptance	Re-run A1–A8 on equivalent audio through the flowgraph

Testable Success Examples

On-air QSO recording demonstrating a real contact worked through RFWhisper on at least 3 different SDRs, posted in release notes.
Contest replay — 30-minute HF contest recording through RFWhisper shows improved busy-band copy without destroying CQ decodes.
VHF FM weak-signal — a scratchy 2m simplex signal becomes readable.

Stretch Goals

gr-rfwhisper OOT module published to PyBOMBS / distro repos
Windows installer bundle (GR 3.10 + RFWhisper)
GNU Radio Companion block palette entry with icon + help
Live waterfall preview in GRC (not full UI yet)

Exit Criteria — v0.2.0

Ship gate

B1–B6 pass. At least 3 on-air recordings from community contributors posted. Hardware matrix doc lists ≥ 5 SDRs with known status (even if "unsupported / needs help").

v0.3 — Mode Profiles + Adaptive Notch

Theme: One denoiser is good; a tuned denoiser per mode is dramatically better. Add mode awareness and a classical notch that cooperates with the NN.

Scope

In:

Mode profiles: ssb, cw, ft8, ft4, rtty, am, fm-narrow, fm-wide, vhf-ssb
Per-mode parameters: attack/release, NN aggressiveness, notch enable, bandwidth, de-emphasis
Adaptive narrowband notch (classical DSP) before the NN — removes carriers / birdies that would otherwise confuse it
Profile auto-detect (optional, conservative)
Latency budget < 50 ms on reference hardware for SSB/CW

Deliverables

rfwhisper/profiles/*.yaml — one per mode
rfwhisper denoise --profile cw CLI flag
Adaptive notch implementation (liquid-dsp iirfilt or VOLK-accelerated LMS)
Profile docs with band/scenario suggestions
Regression tests per profile

Acceptance Criteria

#	Criterion	Threshold
C1	CW profile preserves keying transients	RMS window within ±0.5 dB of raw
C2	FT8 profile: decode count	≥ raw decodes on test cycle; ideal +1 to +3
C3	SSB profile intelligibility (PESQ)	≥ raw PESQ on matched pairs
C4	Adaptive notch removes ≥ 30 dB at target birdie	Measured on synthetic carrier
C5	Latency p99 on SSB/CW profiles	< 50 ms on i5-8xxx / M1
C6	Auto-detect accuracy (if enabled)	≥ 90 % on labeled eval set; off by default

Testable Success Examples

"2m weak-signal SSB" — EME-style weak SSB goes from unreadable to copyable.
"630m CW" — slow CW under atmospheric QRN preserves all keying, crashes reduced.
"FT8 profile never regresses" — 10 hours of replayed FT8 across bands shows zero decode loss.

Stretch Goals

DIGI profile that preserves generic MFSK/PSK shape
Per-profile model variants (tiny model for CW, full model for SSB)
Rig-control (Hamlib) hook to auto-select profile from mode

Exit Criteria — v0.3.0

Ship gate

C1–C5 pass across all profiles. At least one community contributor has validated each profile on real on-air signals.

v0.4 — UI: Before/After Spectrogram + Metrics

Theme: Make the improvement visible. Hams trust their eyes and ears; give them both.

Scope

In:

Qt/PySide6 main window with:
- Dual waterfall (raw vs denoised) with synchronized scroll
- A/B audio toggle (big, obvious)
- Live telemetry: effective SNR gain, SINAD, RTF, CPU %, end-to-end latency
- Model selector + profile selector
- Before/after record button
Optional docked view inside GRC-generated flowgraph
Dark mode, high-contrast mode (for field ops in sunlight)

Deliverables

rfwhisper-gui binary (PySide6)
Waterfall renderer (OpenGL or pyqtgraph)
Metrics panel
Screenshots in docs

Acceptance Criteria

#	Criterion	Threshold
D1	GUI frame rate	≥ 30 fps on reference laptop; ≥ 20 fps on RPi 5
D2	GUI CPU overhead	≤ +10 % CPU vs headless
D3	Telemetry accuracy	SNR gain estimate within ±1 dB of reference
D4	Latency display	Agrees with A4 probe within 5 ms
D5	Accessibility	All controls keyboard-navigable; WCAG AA contrast in light/dark

Testable Success Examples

Screenshot thread on QRZ / r/amateurradio where hams post "look at this waterfall difference".
Field-ops screenshot from a POTA activation showing the UI working on a laptop in bright sun (high-contrast mode).

Stretch Goals

Web-based UI option (for headless server / remote ops)
Recording library browser with ABX test mode
Shareable "clip" export (6-second before/after MP4 with waveform + waterfall)

Exit Criteria — v0.4.0

Ship gate

D1–D5 pass. 3+ screenshots + 1 short demo video in release notes.

v0.5 — Fine-tuning Tools + Dataset Generator

Theme: Give operators the power to tune the model to their noise environment.

Scope

In:

Dataset builder: record clean ham speech + isolated noise, synthesize mixed training data with realistic SNRs
Fine-tuning CLI (rfwhisper train fine-tune) wrapping PyTorch + DFN3 training code
Export to ONNX with validated parity
Evaluation harness (reuse v0.1 acceptance tests)
Docs: "how to record your own noise, train, and deploy in 30 minutes"

Deliverables

rfwhisper train record-noise — captures isolated noise from rig
rfwhisper train build-dataset — synthesizes mixes with SNR control
rfwhisper train fine-tune — LoRA-style or full fine-tune
rfwhisper train export — PyTorch → ONNX with parity check
rfwhisper train evaluate — runs v0.1 acceptance suite on new model
Tutorial notebook in notebooks/
Optional: Hugging Face / self-hosted model hub structure

Acceptance Criteria

#	Criterion	Threshold
E1	Fine-tune wall time (30 min noise sample, RTX 3060)	≤ 2 hours
E2	Fine-tune on RPi 5 CPU (long-running)	Completes in < 24 hours for tiny model
E3	ONNX export parity vs PyTorch	Output diff ≤ 1e-3 RMS on eval set
E4	Post-fine-tune eval	Beats baseline on user's noise by ≥ +2 dB SNR gain; no regression on held-out clean speech
E5	Dataset generator realism	Blind listeners cannot reliably distinguish real vs synthesized mixes

Testable Success Examples

Tutorial: operator records 10 minutes of their solar-inverter noise, fine-tunes for an hour, and demonstrates an additional +2 dB gain over the stock model on their own shack.
Community-submitted model (e.g., "HF-Contester-v1") beats baseline on contest-week recordings.

Stretch Goals

Federated / community averaging of fine-tunes (privacy-preserving; opt-in)
Quantization-aware fine-tuning
LoRA adapters that can be hot-swapped per band

Exit Criteria — v0.5.0

Ship gate

E1–E5 pass. At least 2 community-submitted fine-tunes accepted into the model hub.

v1.0 — Polished Release

Theme: Boring-in-a-good-way. This is the version we tell non-hacker hams to install.

Scope

In:

Signed installers (Windows .msi, macOS .dmg notarized), .deb, .rpm, AUR
Raspberry Pi OS image (bootable SD card) with RFWhisper pre-installed
At least 8 SDRs tested and officially supported
Model hub: minimum 4 community-validated models (HF contest, VHF mobile, POTA portable, 630m / LF)
Extensive docs site (mkdocs or similar)
Performance benchmarks published for laptop + RPi 5 + mid-range desktop
Localization scaffolding (en at minimum; community translations welcomed)

Deliverables

Signed Windows installer
Notarized macOS .dmg (universal binary)
.deb, .rpm, AUR PKGBUILD
RPi OS image + install doc
Model hub (static site + CI validator)
Docs site with tutorials, API reference, architecture
Published benchmark report (reproducible, with hardware + methodology)

Acceptance Criteria

#	Criterion	Threshold
F1	8 supported SDRs tested on-air	Documented in hardware matrix with logs
F2	Installer UX	Non-technical ham installs and gets audio routed in ≤ 15 minutes
F3	Benchmark repro	Any contributor can replicate published numbers ±10 %
F4	All previous acceptance tests pass	A1–A8, B1–B6, C1–C6, D1–D5, E1–E5 green
F5	Zero critical bugs open > 30 days	Tracked in GitHub Projects
F6	Docs completeness	Every CLI flag, every GUI control, every profile documented

Testable Success Examples

A new ham with zero DSP / ML background installs v1.0 on Windows, routes it to WSJT-X, and logs their first FT8 DX through RFWhisper within 1 hour of download.
Contest club publishes testimonial: "RFWhisper made the difference on 80m Saturday night."
At least one magazine / podcast review (QST, SDR-Academy, Ham Radio Crash Course, etc.).

Stretch Goals

Pre-installed on a popular SDR distro (DragonOS, etc.)
Chrome/Firefox extension for browser-based SDR (KiwiSDR, WebSDR)

Exit Criteria — v1.0.0

Ship gate

F1–F6 pass. Release notes include 8 SDR on-air logs, 4 community models, a benchmark report, and at least one external review.

v1.1+ — Plugins, GR4 Native, Propagation Extras

Theme: Expand the platform. Keep v1.0 stable on a long-term support branch; iterate on main.

Candidate Work (prioritized at v1.0 retro)

Plugin architecture: drop-in user models, custom DSP blocks, alternative notchers
GNU Radio 4.0 native block (gr4-rfwhisper) with DAG-native perf wins; keep 3.10 LTS branch
Propagation extras: VOACAP / ionosonde hints to bias the denoiser on expected signal shape
RX-specific modes: digital voice (DMR/D-STAR/YSF audio denoise), EMCOMM voice modes
TX assist: compander / de-noise of your own mic (with heavy ethical guardrails and clear labeling)
Remote shack integration: WFView, FLrig, OpenWebRX pass-through
Federated model improvements with cryptographic verification
Rust rewrite of hot paths where profiling justifies it

Ground Rules for Post-1.0

Main stays green. v1.0 users on an LTS branch never get surprised.
Every new feature needs a testable success metric, same bar as v0.1–v1.0.
Telemetry remains opt-in. Always. No exceptions.
If it doesn't help an actual ham on the air, it waits.

How to Move a Version Forward

Pick an unchecked deliverable above.
Open an issue linking to the relevant acceptance criterion (e.g., "A2: FT8 regression harness").
Propose a design in GitHub Discussions if it's non-trivial.
Submit a PR referencing the issue. CI enforces the applicable acceptance tests.
Request review from the relevant area owner (see AGENTS.md role mapping).
When the last box for a version is checked and all its acceptance tests pass, we cut the tag.

The roadmap is a living document. If you think a milestone is wrong, open an RFC issue. We'd rather change the plan than ship the wrong thing.

73 and keep the signal clean.

Guiding Principles​

Latency Budget (reference)​

v0.1 — Audio-Only Denoiser → Virtual Cable

Scope​

Deliverables​

Acceptance Criteria (Pass/Fail)​

Testable Success Examples​

Stretch Goals​

Exit Criteria — v0.1.0​

v0.2 — GNU Radio + SoapySDR Pipeline

Scope​

Deliverables​

Acceptance Criteria​

Testable Success Examples​

Stretch Goals​

Exit Criteria — v0.2.0​

v0.3 — Mode Profiles + Adaptive Notch

Scope​

Deliverables​

Acceptance Criteria​

Testable Success Examples​

Stretch Goals​

Exit Criteria — v0.3.0​

v0.4 — UI: Before/After Spectrogram + Metrics

Scope​

Deliverables​

Acceptance Criteria​

Testable Success Examples​

Stretch Goals​

Exit Criteria — v0.4.0​

v0.5 — Fine-tuning Tools + Dataset Generator

Scope​

Deliverables​

Acceptance Criteria​

Testable Success Examples​

Stretch Goals​

Exit Criteria — v0.5.0​

v1.0 — Polished Release

Scope​

Deliverables​

Acceptance Criteria​

Testable Success Examples​

Stretch Goals​

Exit Criteria — v1.0.0​

v1.1+ — Plugins, GR4 Native, Propagation Extras

Candidate Work (prioritized at v1.0 retro)​

Ground Rules for Post-1.0​

How to Move a Version Forward​

Guiding Principles

Latency Budget (reference)

Scope

Deliverables

Acceptance Criteria (Pass/Fail)

Testable Success Examples

Stretch Goals

Exit Criteria — v0.1.0

Scope

Deliverables

Acceptance Criteria

Testable Success Examples

Stretch Goals

Exit Criteria — v0.2.0

Scope

Deliverables

Acceptance Criteria

Testable Success Examples

Stretch Goals

Exit Criteria — v0.3.0

Scope

Deliverables

Acceptance Criteria

Testable Success Examples

Stretch Goals

Exit Criteria — v0.4.0

Scope

Deliverables

Acceptance Criteria

Testable Success Examples

Stretch Goals

Exit Criteria — v0.5.0

Scope

Deliverables

Acceptance Criteria

Testable Success Examples

Stretch Goals

Exit Criteria — v1.0.0

Candidate Work (prioritized at v1.0 retro)

Ground Rules for Post-1.0

How to Move a Version Forward