microWakeWord — «Ирина» (Russian)

Files

• irina.tflite — streaming quantized model
• irina.json — v2 manifest (trained_languages: ["ru"], probability_cutoff 0.97, sliding_window_size 5)

Metrics (on real household recordings, not synthetic)

• Recall: 100% (16/16) real «Ирина» recordings at every cutoff through 0.97 (positive mean-prob 0.996) — including congested/child voices earlier models missed.
• FA/h on 3.13 h Russian ambient: 3.5 @0.90 · 1.9 @0.97 · ~1.0 @0.99 (thin sample — tune on device).
• Selected as the best of 15 random-subset runs on real recall (run 4; see repo).

Operating point — tune the cutoff on YOUR device

Why it's alive on real voices (and the French cautionary tale)

• Correct stress, verified per engine. Wrong stress ⇒ mispronounced positives ⇒ dead model. Piper (espeak-ng) is fed plain Cyrillic; Silero is fed «Ир+ина» (+ before the stressed vowel, put_accent=False); Vosk's BERT accentizer handles it from plain text.
• A deliberately diverse, license-clean voice mix. Piper ruslan + Silero (xenia/kseniya/eugene) + a female-leaning Vosk-TTS minority (all Apache/CC-BY-NC-SA). The Vosk female voices restore the female-speaker diversity that a non-redistributable Piper voice would otherwise have provided — so the model generalizes to female speakers without any CC-BY-NC-ND source.
• Selected from a 15-run search, not one lucky run. Recall is stable run-to-run but false- accepts are a data-subset lottery; this artifact is the best of 15 random-subset runs, chosen on real recall/FA — not on the (misleading) synthetic eval.
• Curated hard-negatives — near-misses (Марина / Арина / Ирана / перина, diminutives, inflected forms) teach the model the boundary, not just the word.
• Battle-tested recipe, no heroics — SNR −5…+10 dB (not widened — widening is what broke the French run), negative class weight 20, 3 augmentation rounds, 12k steps.

Usage

Training & license