git:20261027.7bb973c to git:20261027.32ab82f

---

id: local-voice-command-training

name: Train and integrate local wake words and short commands

description: >-

Train or repair a small device-local wake/command detector with shared acoustic

computation, real examples, independent evaluation and correct activation/body

routing. Use for finite command vocabularies, not general speech transcription.

summary: >-

Diagnose the wake/body boundary before retraining, record ready-signalled examples,

keep negative and held-out speech, and deploy the model with its matching router

and supervisor while preserving local actions and ordinary dictation.

type: skill

author: kisa

recommended: false

added: 2026-10-27

tags: [wake-word, kws, training, microwakeword, mini1, commands, stop]

source: https://github.com/kahrendt/microWakeWord

reminder: One activation must open the command body; do not train a second required wake word into every action.

---

# Local wake and command work

Use the user's chosen word, device and interaction contract. For the rationale and

case matrix retrieve `local-wake-word-and-command-training` from Deploychan MCP;

upstream [microWakeWord](https://github.com/kahrendt/microWakeWord) covers wake-model

training, while any multi-command head/router extension must be implemented separately.

## Diagnose before requesting another recording

Read frontend shape/rate, label order, actual model path, native runtime, supervisor

and crop/endpoint logic. Determine whether inference is on the speaker or host.

Trace one failed phrase from audio through candidate scores to execution/dictation.

Check wake consumption, pre-roll, body-window boundaries, confidence/margin, pause

gate and a stale deployment before attributing every miss to the training samples.

Use a shared frontend/network when it suits a small vocabulary. Do not present that

custom adaptation as generic ASR or promise capacity for an arbitrary number of commands.

Desktop actions can use host ASR and a deterministic parser without expanding this model.

## Collect and evaluate

1. Establish capture readiness; then tell the user to speak. Preserve a session ID,

label, intended phrase, source recording and train/validation assignment.

2. During collection disable effects and outgoing agent dictation. Collect normal,

fast, quiet and distant variants, short/long aliases, confusable negatives and

ordinary sentences mentioning the action words. Keep recordings private.

3. Retain independent recordings outside training. Record when tuning against a

validation set turns it into a regression set; do not claim independent accuracy.

4. Evaluate both one-breath and wake/pause/body forms, repeated wake, bare commands,

command mentions and silence. Compare the same WAV on host and target runtime.

State any simulated VAD/ASR events. Respect the user's limits on test execution.

## Integrate the accepted interaction

One wake opens a bounded continuation session. Accept a body without another name,

consume a successful local command and send only ordinary dictation onward. Keep

volume/Bluetooth execution and short cues on the device when local autonomy is the

goal; do not silently substitute a chat agent issuing ADB commands.

Stop words have a separate pause/false-positive trade-off. Preserve the selected

policy. During own playback, acoustic detections are candidates until the processed

audio/ASR confirmation accepts stop; see `buffered-speech-and-cancellation` for the

state/queue requirements. Preserve physical mute priority and current volume.

Version and deploy the model, labels, frontend, native protocol and host handling as

a compatible set with rollback. Report the actually loaded version, misses/false

actions and authorized verification; a successful build or copy alone is not completion.