Release NVIDIA WakeWord Trainer v13

Release NVIDIA WakeWord Trainer v12
2026-08-12 16:05:34 -06:00 · 2026-07-17 20:38:18 -05:00 · 2026-07-17 08:21:50 -05:00
13 changed files with 1895 additions and 34 deletions
--- a/.github/workflows/docker-publish.yml
+++ b/.github/workflows/docker-publish.yml
@@ -26,6 +26,18 @@ jobs:
      - name: Check out repository
        uses: actions/checkout@v4

+      - name: Validate tag matches trainer version
+        if: startsWith(github.ref, 'refs/tags/')
+        shell: bash
+        run: |
+          set -euo pipefail
+          version="$(tr -d '[:space:]' < VERSION)"
+          expected_tag="v${version#v}"
+          if [[ "${GITHUB_REF_NAME}" != "${expected_tag}" ]]; then
+            echo "Tag ${GITHUB_REF_NAME} does not match trainer version ${expected_tag}." >&2
+            exit 1
+          fi
+
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

@@ -94,6 +106,7 @@ jobs:
          title="microWakeWord Nvidia Trainer ${TAG_NAME}"
          generated_notes="$(mktemp)"
          release_notes="$(mktemp)"
+          test -s WHATS_NEW.md

          gh api "repos/${REPO}/releases/generate-notes" \
            -f tag_name="${TAG_NAME}" \
@@ -101,6 +114,11 @@ jobs:
            --jq '.body' > "${generated_notes}"

          {
+            echo "## What's New"
+            echo
+            cat WHATS_NEW.md
+            echo
+            echo
            echo "## Docker Images"
            echo
            echo "- \`ghcr.io/tatertotterson/microwakeword:${TAG_NAME}\`"
--- a/README.md
+++ b/README.md
@@ -22,15 +22,17 @@ docker pull ghcr.io/tatertotterson/microwakeword:latest
 Tagged releases also publish matching immutable image tags:

 ```bash
-docker pull ghcr.io/tatertotterson/microwakeword:v11
+docker pull ghcr.io/tatertotterson/microwakeword:v13
 ```

+The release tag must match `VERSION`. Update `WHATS_NEW.md` before tagging; the Docker workflow prepends it to GitHub's automatically generated release notes.
+
 RTX 50-series / Blackwell GPUs use a separate image with CUDA 12.8 and a
 Python 3.13 TensorFlow build for `sm_120`:

 ```bash
 docker pull ghcr.io/tatertotterson/microwakeword:blackwell
-docker pull ghcr.io/tatertotterson/microwakeword:v11-blackwell
+docker pull ghcr.io/tatertotterson/microwakeword:v13-blackwell
 ```

 Use the Blackwell image only for RTX 50-series cards. It includes the
@@ -51,9 +53,9 @@ docker run -d \
  ghcr.io/tatertotterson/microwakeword:latest
 ```

-Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v11` when you want to pin a known release instead of tracking `latest`.
+Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v13` when you want to pin a known release instead of tracking `latest`.
 For RTX 50-series cards, use `ghcr.io/tatertotterson/microwakeword:blackwell`
-or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v11-blackwell`
+or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v13-blackwell`
 in the same `docker run` command.

 The flags:
@@ -78,6 +80,7 @@ If you change `REC_PORT`, open that port instead and use the same port in the sa
 ## What The UI Does

 - `Trainer` starts a wake-word session, shows positive/negative sample counts, and launches training.
+- `Auto Training` transcribes real wake triggers, promotes phrase-misses to hard negatives, schedules retraining, and refreshes Tater Native satellites.
 - `Captured Audio` reviews clips sent by Tater Native or ESPHome sats, including wake hits, close misses, and false wakes.
 - `Samples` plays, removes, clears, and manually imports personal or negative samples.
 - `Wake Words` lists locally trained JSON/model links for live wake-word switching in Tater.
@@ -162,6 +165,34 @@ Starting a new session does not clear samples. Use the clear buttons in `Samples

 ---

+## Auto Training
+
+`Auto Training` is an opt-in sample-review and retraining loop. It is disabled until you enter the exact wake phrase and enable it.
+
+For each new wake-trigger clip sent to the trainer:
+
+1. Faster Whisper transcribes the audio locally.
+2. If the transcript contains the configured wake phrase, the clip stays in `Captured Audio` for manual review by default.
+3. If speech was transcribed but the wake phrase is absent, the clip moves to `/data/negative_samples/` as an auto-reviewed hard negative.
+4. Empty transcripts, VAD-blocked captures, and captures for another wake word stay out of the automatic negative path.
+
+Two optional cleanup rules are available:
+
+- `Delete confirmed good wakes` removes normal wake-trigger clips after STT confirms the configured phrase.
+- `Promote confirmed close misses` checks close misses that passed VAD and moves them to the personal positive samples only when STT confirms the configured phrase.
+
+A close miss with an empty transcript or without the configured phrase stays in `Captured Audio`; it is never turned into a negative automatically. Saving Auto Training settings also scans existing eligible captures. Enabling close-miss promotion reviews previous unreviewed close misses, while enabling cleanup removes previously confirmed good wakes without transcribing them a second time.
+
+The default `small.en` model uses CUDA with `float16` when CTranslate2 can see an NVIDIA GPU, and falls back to CPU with `int8`. Choose a multilingual Faster Whisper model such as `small` when the wake phrase is not English. Downloaded STT models are cached in `/data/auto_train_models/`.
+
+Scheduled training runs only after the configured number of new automatic negatives has accumulated. A successful run publishes the replacement model at the same wake-word URL and can call Tater's native satellite settings API to make connected satellites pull it again. This refresh uses the existing Tater Native update path, so no satellite firmware change is required.
+
+The `Trainer public URL` must be reachable from the satellites. With the documented `--network host` command, the trainer can normally use the LAN address from the browser request or host network. If you open the UI as `http://localhost:8789`, enter a value such as `http://192.168.1.50:8789`, or start the container with `REC_PUBLIC_BASE_URL` set to that value. When using Docker bridge networking, always set this URL to the published host address; a container bridge address is not satellite-reachable.
+
+The default Tater URL, `http://127.0.0.1:8501`, assumes the documented host networking. Change it to a container-reachable Tater address if you use another Docker network. The optional API token is stored in `/data/auto_train_config.json` with owner-only permissions.
+
+---
+
 ## Training Flow

 1. Enter the wake phrase in `Trainer`.
@@ -212,6 +243,7 @@ After those assets are prepared, later runs reuse the local copies unless the mo
 The `Wake Words` tab lists locally trained wake-word packages from `/data/trained_wake_words/`.

 - Copy the JSON URL into the Tater Native satellite settings to switch wake words live.
+- Links use the configured public trainer URL, a non-loopback browser host, or the detected LAN address instead of advertising `127.0.0.1` to satellites.
 - Open the JSON or model links directly for quick inspection.
 - The JSON includes the matching model path plus Tater tuning metadata.
 - No firmware flashing happens from this trainer app anymore.
@@ -244,7 +276,7 @@ The JSON keeps the standard microWakeWord fields for compatibility:
 {
  "micro": {
    "probability_cutoff": 0.97,
-    "sliding_window_size": 5
+    "sliding_window_size": 6
  }
 }
 ```
@@ -259,8 +291,8 @@ It also includes Tater Native metadata used by newer satellites and the Tater se
  "tater_native": {
    "format_version": 1,
    "wake_threshold": 0.97,
-    "wake_sliding_window": 5,
-    "close_miss_threshold": 0.78,
+    "wake_sliding_window": 6,
+    "close_miss_threshold": 0.80,
    "frontend": {
      "name": "tflm_microfrontend",
      "sample_rate": 16000,
@@ -273,6 +305,7 @@ It also includes Tater Native metadata used by newer satellites and the Tater se
 ```

 Calibration metrics are included under `calibration` so false accepts/hour and recall can be surfaced in the UI.
+Calibration evaluates thresholds from `0.95` through `1.00` with sliding windows of `5`, `6`, and `7`. Among candidates within 0.5 percentage points of the best recall, it prefers the lowest measured ambient false-accept rate. If calibration cannot complete, packaging uses the conservative `0.97` threshold and a window of `6`.

 ---

@@ -289,6 +322,7 @@ That removes:
 - cached datasets
 - training environments
 - trained models
+- Auto Training settings, state, transcripts, and cached Faster Whisper models

 ---

@@ -296,6 +330,7 @@ That removes:

 - Personal samples are optional.
 - Negative samples are optional but useful for reducing false wakes.
+- Auto Training is disabled by default and only classifies actual wake triggers automatically.
 - The UI server is `trainer_server.py`.
 - The launcher is `run.sh`.
 - Trainer capture settings live in Tater for Tater Native satellites, and on device entities for older ESPHome satellites.
--- a/1
+++ b/1
@@ -0,0 +1 @@
+13
--- a/WHATS_NEW.md
+++ b/WHATS_NEW.md
@@ -0,0 +1,6 @@
+- Added opt-in Auto Training for false-positive wake triggers, using Faster Whisper with automatic CUDA/float16 selection and CPU/int8 fallback.
+- Wake triggers whose transcripts do not contain the configured phrase can now become hard negatives automatically; empty transcripts and phrase matches remain available for manual review unless optional cleanup is enabled.
+- Added optional confirmed-good-wake cleanup and conservative close-miss recovery that promotes only VAD-approved, STT-confirmed phrase matches to positive samples.
+- Added scheduled retraining with a minimum-new-negatives threshold and automatic Tater Native satellite refresh after a successful model build.
+- Wake-word download links now advertise a LAN-reachable trainer URL instead of `127.0.0.1`.
+- Tightened detector calibration defaults to favor fewer ambient false accepts while preserving candidates within 0.5 percentage points of the best recall.
--- a/cli/calibrate_detector.py
+++ b/cli/calibrate_detector.py
@@ -9,24 +9,22 @@ import math
 import os
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Iterable, Sequence
+from typing import Any, Iterable, Sequence

 import numpy as np
 import yaml

-from microwakeword.data import FeatureHandler
-from microwakeword.inference import Model
-
-
-DEFAULT_WINDOW_SIZES = [4, 5, 6, 7]
+DEFAULT_WINDOW_SIZES = [5, 6, 7]
 DEFAULT_TARGET_FAPH = float(os.environ.get("MWW_CALIBRATION_TARGET_FAPH", "0.25"))
 DEFAULT_COOLDOWN_SLICES = int(os.environ.get("MWW_CALIBRATION_COOLDOWN_SLICES", "25"))
 DEFAULT_POSITIVE_SKIP_SLICES = int(
    os.environ.get("MWW_CALIBRATION_POSITIVE_SKIP_SLICES", "25")
 )
 DEFAULT_CUTOFF_STEP = float(os.environ.get("MWW_CALIBRATION_CUTOFF_STEP", "0.01"))
-DEFAULT_CUTOFF_MIN = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MIN", "0.85"))
+DEFAULT_CUTOFF_MIN = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MIN", "0.95"))
 DEFAULT_CUTOFF_MAX = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MAX", "1.00"))
+DEFAULT_RECALL_MARGIN = float(os.environ.get("MWW_CALIBRATION_RECALL_MARGIN", "0.005"))
+PREFERRED_WINDOW_SIZE = 6


 def parse_args() -> argparse.Namespace:
@@ -65,6 +63,15 @@ def parse_args() -> argparse.Namespace:
        default=DEFAULT_TARGET_FAPH,
        help="Target ambient false accepts per hour for the selected operating point.",
    )
+    parser.add_argument(
+        "--recall-margin",
+        type=float,
+        default=DEFAULT_RECALL_MARGIN,
+        help=(
+            "Maximum recall loss allowed when preferring a candidate with fewer "
+            "ambient false accepts (0.005 means 0.5 percentage points)."
+        ),
+    )
    parser.add_argument(
        "--cooldown-slices",
        type=int,
@@ -159,7 +166,13 @@ def _compute_false_accepts_per_hour(
 def _select_best_candidate(
    candidates: list[dict[str, float]],
    target_faph: float,
+    recall_margin: float = DEFAULT_RECALL_MARGIN,
 ) -> tuple[dict[str, float], float]:
+    if not candidates:
+        raise ValueError("at least one calibration candidate is required")
+    if recall_margin < 0:
+        raise ValueError("recall margin must be >= 0")
+
    fallback_limits = [
        target_faph,
        max(target_faph * 2.0, target_faph + 0.5),
@@ -172,13 +185,27 @@ def _select_best_candidate(
                return index
        return len(fallback_limits)

+    # Stay in the strictest false-accept tier that has a viable candidate. Within
+    # that tier, keep candidates close to the best recall, then spend the allowed
+    # recall margin on the lowest measured false-accept rate.
+    best_tier = min(tier(candidate) for candidate in candidates)
+    tier_candidates = [
+        candidate for candidate in candidates if tier(candidate) == best_tier
+    ]
+    best_recall = max(candidate["recall"] for candidate in tier_candidates)
+    recall_floor = best_recall - recall_margin
+    viable_candidates = [
+        candidate
+        for candidate in tier_candidates
+        if candidate["recall"] >= recall_floor - 1e-12
+    ]
+
    best = min(
-        candidates,
+        viable_candidates,
        key=lambda candidate: (
-            tier(candidate),
-            -candidate["recall"],
            candidate["false_accepts_per_hour"],
-            abs(candidate["sliding_window_size"] - 5),
+            -candidate["recall"],
+            abs(candidate["sliding_window_size"] - PREFERRED_WINDOW_SIZE),
            -candidate["probability_cutoff"],
        ),
    )
@@ -195,7 +222,7 @@ def _load_config(config_path: Path) -> dict:


 def _load_eval_sets(
-    handler: FeatureHandler,
+    handler: Any,
    config: dict,
 ) -> tuple[str, str, list[np.ndarray], list[np.ndarray]]:
    for positive_mode, ambient_mode in (
@@ -228,7 +255,7 @@ def _load_eval_sets(


 def _predict_tracks(
-    model: Model,
+    model: Any,
    tracks: Sequence[np.ndarray],
    label: str,
 ) -> list[np.ndarray]:
@@ -244,8 +271,13 @@ def _predict_tracks(


 def main() -> int:
+    from microwakeword.data import FeatureHandler
+    from microwakeword.inference import Model
+
    args = parse_args()
    window_sizes = _parse_window_sizes(args.window_sizes)
+    if args.recall_margin < 0 or args.recall_margin > 1:
+        raise ValueError("recall-margin must be between 0 and 1")
    if args.cutoff_step <= 0:
        raise ValueError("cutoff-step must be > 0")
    if args.cutoff_max < args.cutoff_min:
@@ -276,6 +308,10 @@ def main() -> int:
        f"→ Evaluating window sizes {window_sizes} with target <= "
        f"{args.target_faph:.2f} false accepts/hour"
    )
+    print(
+        f"→ Favoring lower false accepts within "
+        f"{args.recall_margin:.2%} of the best recall"
+    )

    config = _load_config(config_path)
    config["flags"] = config.get("flags", {})
@@ -338,7 +374,11 @@ def main() -> int:
            candidates.append(candidate)
            window_candidates.append(candidate)

-        best_window, _ = _select_best_candidate(window_candidates, args.target_faph)
+        best_window, _ = _select_best_candidate(
+            window_candidates,
+            args.target_faph,
+            args.recall_margin,
+        )
        best_by_window.append(best_window)
        print(
            "   window={window}: cutoff={cutoff:.2f}; recall={recall:.2%}; "
@@ -350,7 +390,11 @@ def main() -> int:
            )
        )

-    best, selected_limit = _select_best_candidate(candidates, args.target_faph)
+    best, selected_limit = _select_best_candidate(
+        candidates,
+        args.target_faph,
+        args.recall_margin,
+    )
    if best["false_accepts_per_hour"] > args.target_faph + 1e-9:
        print(
            "⚠️  No candidate met the target false accepts/hour budget; "
@@ -390,6 +434,8 @@ def main() -> int:
            "cutoff_min": round(float(cutoffs[0]), 4),
            "cutoff_max": round(float(cutoffs[-1]), 4),
            "cutoff_step": float(args.cutoff_step),
+            "recall_margin": float(args.recall_margin),
+            "preferred_window_size": PREFERRED_WINDOW_SIZE,
        },
        "per_window_best": best_by_window,
        "generated_at": datetime.now(timezone.utc).isoformat(),
--- a/cli/wake_word_sample_trainer
+++ b/cli/wake_word_sample_trainer
@@ -467,7 +467,12 @@ echo "🎯 Calibrating detector settings for on-device use…"
 if "${PYTHON_BIN:-python}" "${PROGDIR}/calibrate_detector.py" \
    --training-config "${WORK_DIR}/trained_models/wakeword/training_config.yaml" \
    --model "${source_path}" \
-    --output "${calibration_path}"; then
+    --output "${calibration_path}" \
+    --target-faph "${MWW_CALIBRATION_TARGET_FAPH:-0.25}" \
+    --recall-margin "${MWW_CALIBRATION_RECALL_MARGIN:-0.005}" \
+    --window-sizes "${MWW_CALIBRATION_WINDOW_SIZES:-5,6,7}" \
+    --cutoff-min "${MWW_CALIBRATION_CUTOFF_MIN:-0.95}" \
+    --cutoff-max "${MWW_CALIBRATION_CUTOFF_MAX:-1.00}"; then
    echo "✅ Detector calibration complete."
 else
    echo "⚠️  Detector calibration failed; packaging with default detector settings."
@@ -496,8 +501,8 @@ from pathlib import Path
 json_path = Path(os.environ["JSON_PATH"])
 calibration_path = Path(os.environ.get("CALIBRATION_PATH", ""))
 language = (os.environ.get("LANGUAGE", "en") or "en").strip().lower()
-probability_cutoff = 0.85
-sliding_window_size = 4
+probability_cutoff = 0.97
+sliding_window_size = 6
 strict_min_close_miss_threshold = 0.68
 calibration = {}

--- a/2
+++ b/2
@@ -6,7 +6,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 # System deps
 RUN apt-get update && apt-get install -y --no-install-recommends \
    python3.12 python3.12-venv python3.12-dev python3-pip python-is-python3 \
-    git wget curl unzip patch ninja-build ca-certificates nano less \
+    git wget curl unzip patch ninja-build ca-certificates nano less libgomp1 \
 && rm -rf /var/lib/apt/lists/* \
 && mkdir -p /data

--- a/dockerfile.blackwell
+++ b/dockerfile.blackwell
@@ -13,7 +13,7 @@ ENV MWW_BLACKWELL_TF_WHEEL_URL=https://github.com/chivitiH/tensorflow-blackwell-
 # Python 3.13 is used only for the Blackwell TensorFlow training step.
 RUN apt-get update && apt-get install -y --no-install-recommends \
    software-properties-common ca-certificates curl git wget unzip patch \
-    ninja-build nano less \
+    ninja-build nano less libgomp1 \
 && add-apt-repository -y ppa:deadsnakes/ppa \
 && apt-get update \
 && apt-get install -y --no-install-recommends \
--- a/run.sh
+++ b/run.sh
@@ -31,7 +31,10 @@ install_ui_deps() {
    "uvicorn[standard]==${UVICORN_VERSION}" \
    "python-multipart==${PY_MULTIPART_VERSION}" \
    "silero-vad>=5.0.0" \
-    "numpy>=1.24.0"
+    "numpy>=1.24.0" \
+    "faster-whisper>=1.0.0" \
+    "nvidia-cublas-cu12" \
+    "nvidia-cudnn-cu12==9.*"
 }

 # -----------------------------
@@ -78,8 +81,13 @@ exact = {
 minimum = {
    "silero-vad": "5.0.0",
    "numpy": "1.24.0",
+    "faster-whisper": "1.0.0",
+    "nvidia-cudnn-cu12": "9.0.0",
 }
-present = ("torch",)
+present = (
+    "torch",
+    "nvidia-cublas-cu12",
+)

 for package, expected in exact.items():
    if md.version(package) != expected:
@@ -95,6 +103,28 @@ PY
    install_ui_deps
  fi
 fi
+
+# Faster Whisper/CTranslate2 loads these CUDA libraries before Python starts.
+# They live in the persistent UI venv so both Docker image variants can use GPU STT.
+WHISPER_CUDA_LIBRARY_PATH="$("${PY}" - <<'PY'
+import os
+
+try:
+    import nvidia.cublas.lib
+    import nvidia.cudnn.lib
+except ImportError:
+    print("")
+else:
+    print(
+        os.path.dirname(nvidia.cublas.lib.__file__)
+        + ":"
+        + os.path.dirname(nvidia.cudnn.lib.__file__)
+    )
+PY
+)"
+if [[ -n "${WHISPER_CUDA_LIBRARY_PATH}" ]]; then
+  export LD_LIBRARY_PATH="${WHISPER_CUDA_LIBRARY_PATH}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
+fi
 # -----------------------------
 # Trainer server env
 # -----------------------------
--- a/static/index.html
+++ b/static/index.html
@@ -210,6 +210,35 @@
      width: 100%;
    }

+    .autoGrid {
+      display: grid;
+      grid-template-columns: repeat(2, minmax(0, 1fr));
+      gap: 14px;
+    }
+    .autoGrid .wide { grid-column: 1 / -1; }
+    .checkField {
+      display: flex;
+      align-items: flex-start;
+      gap: 10px;
+      padding: 12px;
+      border-radius: 12px;
+      border: 1px solid rgba(255,255,255,0.09);
+      background: rgba(255,255,255,0.035);
+      color: var(--muted);
+      line-height: 1.4;
+    }
+    .checkField input { margin-top: 3px; }
+    .checkField[hidden] { display: none !important; }
+    .checkField strong { color: var(--text); display: block; margin-bottom: 3px; }
+    .autoActions { display: flex; flex-wrap: wrap; gap: 10px; }
+    .autoAudit {
+      padding: 14px;
+      border-radius: 14px;
+      border: 1px solid rgba(255,255,255,0.08);
+      background: rgba(0,0,0,0.22);
+      overflow-wrap: anywhere;
+    }
+
    .firmwareGrid {
      display: grid;
      grid-template-columns: minmax(260px, 1fr) minmax(160px, 220px) minmax(220px, 280px);
@@ -1074,6 +1103,8 @@
      input[type="text"] { width: 100%; }
      .fileItem { align-items: flex-start; flex-direction: column; }
      .firmwareGrid { grid-template-columns: 1fr; }
+      .autoGrid { grid-template-columns: 1fr; }
+      .autoGrid .wide { grid-column: auto; }
      .firmwareLayout,
      .firmwareTargetGrid,
      .firmwareActionsPanel {
@@ -1164,6 +1195,7 @@

    <div class="tabs">
      <button id="tabTrainer" class="tabBtn active" type="button">Trainer</button>
+      <button id="tabAuto" class="tabBtn" type="button">Auto Training</button>
      <button id="tabFirmware" class="tabBtn" type="button">Wake Words</button>
      <button id="tabCaptured" class="tabBtn" type="button">Captured Audio</button>
      <button id="tabSamples" class="tabBtn" type="button">Samples</button>
@@ -1250,6 +1282,168 @@
      </section>
    </div>

+    <div id="autoView" class="viewStack stack" hidden>
+      <div class="card studioHero trainerHero">
+        <div class="row space">
+          <div>
+            <div class="studioKicker">False-Positive Loop</div>
+            <h3>Auto Training</h3>
+            <p>Sort false wakes into negatives, recover spoken close misses as positives, clean up confirmed wakes, retrain on your schedule, and refresh connected satellites.</p>
+            <div class="studioSteps" aria-label="Auto Training steps">
+              <span class="studioStepChip"><b>1</b> Transcribe wakes</span>
+              <span class="studioStepChip"><b>2</b> Collect negatives</span>
+              <span class="studioStepChip"><b>3</b> Retrain + refresh</span>
+            </div>
+          </div>
+          <span id="autoStatus" class="pill">Disabled</span>
+        </div>
+      </div>
+
+      <section class="card studioPanel stack">
+        <div class="studioPanelHeader">
+          <div class="studioPanelTitle">
+            <span class="studioStepBadge">1</span>
+            <div>
+              <h3>Review Rules</h3>
+              <p>False-wake sorting is always conservative. Cleanup and close-miss promotion remain optional, and empty STT results stay in the inbox.</p>
+            </div>
+          </div>
+        </div>
+        <label class="checkField">
+          <input id="autoEnabled" type="checkbox" />
+          <span><strong>Enable Auto Training</strong>Eligible wake triggers will be queued for local Faster Whisper transcription.</span>
+        </label>
+        <label class="checkField">
+          <input id="autoDeleteConfirmedWakes" type="checkbox" />
+          <span><strong>Delete confirmed good wakes</strong>When a normal wake trigger contains the configured phrase, remove it from Captured Audio instead of keeping it for manual review.</span>
+        </label>
+        <label class="checkField">
+          <input id="autoPromoteCloseMisses" type="checkbox" />
+          <span><strong>Promote confirmed close misses</strong>Transcribe close misses that passed VAD and move them to Positive samples only when STT finds the configured phrase.</span>
+        </label>
+        <div class="autoGrid">
+          <label class="field">
+            <strong>Wake phrase</strong>
+            <input id="autoWakePhrase" type="text" placeholder='e.g. "hey tater"' />
+          </label>
+          <label class="field">
+            <strong>STT language</strong>
+            <input id="autoLanguage" type="text" value="en" placeholder="en" />
+          </label>
+          <label class="field wide">
+            <strong>Faster Whisper model</strong>
+            <input id="autoSttModel" type="text" value="small.en" placeholder="small.en" />
+          </label>
+          <label class="field">
+            <strong>STT device</strong>
+            <select id="autoSttDevice">
+              <option value="auto" selected>Auto (prefer CUDA)</option>
+              <option value="cuda">CUDA</option>
+              <option value="cpu">CPU</option>
+            </select>
+          </label>
+          <label class="field">
+            <strong>Compute type</strong>
+            <select id="autoSttComputeType">
+              <option value="auto" selected>Auto (float16 CUDA / int8 CPU)</option>
+              <option value="float16">float16</option>
+              <option value="int8_float16">int8_float16</option>
+              <option value="int8">int8</option>
+              <option value="float32">float32</option>
+              <option value="default">CTranslate2 default</option>
+            </select>
+          </label>
+          <label class="field">
+            <strong>Minimum transcript characters</strong>
+            <input id="autoMinimumChars" type="number" min="1" max="100" value="2" />
+          </label>
+        </div>
+      </section>
+
+      <section class="card studioPanel stack">
+        <div class="studioPanelHeader">
+          <div class="studioPanelTitle">
+            <span class="studioStepBadge">2</span>
+            <div>
+              <h3>Training Schedule</h3>
+              <p>A scheduled run starts only after enough new auto-reviewed negatives have accumulated.</p>
+            </div>
+          </div>
+        </div>
+        <div class="autoGrid">
+          <label class="field">
+            <strong>Run training</strong>
+            <select id="autoScheduleHours">
+              <option value="0">Manually only</option>
+              <option value="6">Every 6 hours</option>
+              <option value="12">Every 12 hours</option>
+              <option value="24" selected>Every day</option>
+              <option value="48">Every 2 days</option>
+              <option value="168">Every week</option>
+            </select>
+          </label>
+          <label class="field">
+            <strong>Minimum new negatives</strong>
+            <input id="autoMinimumNegatives" type="number" min="1" max="10000" value="3" />
+          </label>
+        </div>
+        <div class="statGrid">
+          <div class="stat"><span class="label">Pending Negatives</span><span class="value" id="autoPendingNegatives">0</span></div>
+          <div class="stat"><span class="label">Next Scheduled Check</span><span class="value" id="autoNextRun" style="font-size:16px;">Manual</span></div>
+          <div class="stat"><span class="label">Last Training</span><span class="value" id="autoLastTraining" style="font-size:16px;">Never</span></div>
+        </div>
+      </section>
+
+      <section class="card studioPanel stack">
+        <div class="studioPanelHeader">
+          <div class="studioPanelTitle">
+            <span class="studioStepBadge">3</span>
+            <div>
+              <h3>Publish + Satellite Refresh</h3>
+              <p>The trainer publishes a LAN-reachable model URL, then asks Tater to re-push live settings so the firmware downloads the updated model at the same URL.</p>
+            </div>
+          </div>
+        </div>
+        <div class="autoGrid">
+          <label class="field wide">
+            <strong>Trainer public URL</strong>
+            <input id="autoAdvertisedUrl" type="text" placeholder="Auto-detect this host's LAN IP" />
+            <span id="autoDetectedUrl" class="muted">Auto-detected when left blank.</span>
+          </label>
+          <label class="field wide">
+            <strong>Tater URL</strong>
+            <input id="autoTaterUrl" type="text" value="http://127.0.0.1:8501" />
+          </label>
+          <label class="field">
+            <strong>Satellite selector (optional)</strong>
+            <input id="autoTaterSelector" type="text" placeholder="Blank refreshes all connected sats" />
+          </label>
+          <label class="field">
+            <strong>Tater API token (if enabled)</strong>
+            <input id="autoTaterToken" type="password" placeholder="Not configured" autocomplete="off" />
+          </label>
+        </div>
+        <label class="checkField">
+          <input id="autoNotifySatellites" type="checkbox" checked />
+          <span><strong>Refresh satellites after successful training</strong>Uses Tater's existing native satellite settings API.</span>
+        </label>
+        <label id="autoClearTokenRow" class="checkField" hidden>
+          <input id="autoClearTaterToken" type="checkbox" />
+          <span><strong>Clear the saved Tater token</strong>The token is otherwise preserved when the password field is blank.</span>
+        </label>
+      </section>
+
+      <section class="card studioPanel stack">
+        <div class="autoActions">
+          <button id="autoSaveBtn" class="primary" type="button">Save Auto Training</button>
+          <button id="autoReviewNowBtn" type="button">Review inbox now</button>
+          <button id="autoTrainNowBtn" type="button">Train now</button>
+          <button id="autoNotifyNowBtn" type="button">Refresh satellites now</button>
+        </div>
+        <div id="autoAudit" class="autoAudit muted">No automatic review has run yet.</div>
+      </section>
+    </div>
+
    <div id="capturedView" class="viewStack stack" hidden>
      <div class="card studioHero captureHero">
        <div class="row space">
@@ -1498,9 +1692,11 @@
    captured: { items: [], captured_count: 0, negative_count: 0, personal_count: 0 },
    samples: { personal: [], negative: [], personal_count: 0, negative_count: 0, activeBucket: "personal", pages: { personal: 0, negative: 0 } },
    firmware: { wakeWords: [] },
+    autoTrain: null,
    uploadBusy: false,
    reviewBusy: false,
    firmwareBusy: false,
+    autoBusy: false,
    trainingPoller: null,
    activeView: "trainer",
  };
@@ -1774,12 +1970,14 @@
  }

  function setActiveView(view) {
-    uiState.activeView = ["captured", "samples", "firmware"].includes(view) ? view : "trainer";
+    uiState.activeView = ["auto", "captured", "samples", "firmware"].includes(view) ? view : "trainer";
    $("trainerView").hidden = uiState.activeView !== "trainer";
+    $("autoView").hidden = uiState.activeView !== "auto";
    $("capturedView").hidden = uiState.activeView !== "captured";
    $("samplesView").hidden = uiState.activeView !== "samples";
    $("firmwareView").hidden = uiState.activeView !== "firmware";
    $("tabTrainer").classList.toggle("active", uiState.activeView === "trainer");
+    $("tabAuto").classList.toggle("active", uiState.activeView === "auto");
    $("tabCaptured").classList.toggle("active", uiState.activeView === "captured");
    $("tabSamples").classList.toggle("active", uiState.activeView === "samples");
    $("tabFirmware").classList.toggle("active", uiState.activeView === "firmware");
@@ -1835,6 +2033,147 @@
    return Number.isNaN(parsed.getTime()) ? String(value) : parsed.toLocaleString();
  }

+  function renderAutoTrain(payload, populateForm = true) {
+    const data = payload || {};
+    const config = data.config || {};
+    const state = data.state || {};
+    const runtime = data.runtime || {};
+    uiState.autoTrain = data;
+
+    if (populateForm) {
+      $("autoEnabled").checked = Boolean(config.enabled);
+      $("autoWakePhrase").value = config.wake_phrase || uiState.session?.raw_phrase || "";
+      $("autoLanguage").value = config.language || uiState.session?.language || "en";
+      $("autoSttModel").value = config.stt_model || "small.en";
+      $("autoSttDevice").value = config.stt_device || "auto";
+      $("autoSttComputeType").value = config.stt_compute_type || "auto";
+      $("autoMinimumChars").value = String(config.minimum_transcript_chars ?? 2);
+      $("autoDeleteConfirmedWakes").checked = Boolean(config.delete_confirmed_wakes);
+      $("autoPromoteCloseMisses").checked = Boolean(config.promote_close_misses);
+      $("autoScheduleHours").value = String(config.schedule_hours ?? 24);
+      $("autoMinimumNegatives").value = String(config.minimum_new_negatives ?? 3);
+      $("autoAdvertisedUrl").value = config.advertised_base_url || "";
+      $("autoTaterUrl").value = config.tater_url || "http://127.0.0.1:8501";
+      $("autoTaterSelector").value = config.tater_selector || "";
+      $("autoTaterToken").value = "";
+      $("autoTaterToken").placeholder = config.tater_api_token_configured ? "Saved token (leave blank to keep)" : "Not configured";
+      $("autoNotifySatellites").checked = config.notify_satellites !== false;
+      $("autoClearTokenRow").hidden = !config.tater_api_token_configured;
+      $("autoClearTaterToken").checked = false;
+    }
+
+    $("autoDetectedUrl").textContent = config.advertised_base_url
+      ? `Using configured URL: ${config.advertised_base_url}`
+      : `Auto-detected URL: ${data.advertised_base_url || "unavailable"}`;
+    $("autoPendingNegatives").textContent = String(Number(state.pending_negative_count || 0));
+    $("autoNextRun").textContent = state.next_run_at ? formatTimestamp(state.next_run_at) : "Manual";
+    const lastExit = state.last_train_exit_code;
+    $("autoLastTraining").textContent = state.last_train_finished_at
+      ? `${formatTimestamp(state.last_train_finished_at)}${lastExit === null || lastExit === undefined ? "" : ` · exit ${lastExit}`}`
+      : "Never";
+
+    if (runtime.review_running) {
+      setPill($("autoStatus"), `Transcribing ${runtime.review_file || "wake"}`, "warn");
+    } else if (uiState.training?.running && config.enabled) {
+      setPill($("autoStatus"), "Training running", "warn");
+    } else if (config.enabled) {
+      setPill($("autoStatus"), "Enabled", "ok");
+    } else {
+      setPill($("autoStatus"), "Disabled", "");
+    }
+
+    const audit = [];
+    if (state.last_review_result) audit.push(`Last review: ${String(state.last_review_result).replaceAll("_", " ")}`);
+    if (state.last_review_file) audit.push(state.last_review_file);
+    if (state.last_review_transcript) audit.push(`STT: “${state.last_review_transcript}”`);
+    if (state.last_review_error) audit.push(`Error: ${state.last_review_error}`);
+    if (state.last_stt_device) audit.push(`STT runtime: ${state.last_stt_device} / ${state.last_stt_compute_type || "default"}`);
+    if (state.last_notify_at) {
+      audit.push(state.last_notify_error
+        ? `Satellite refresh failed: ${state.last_notify_error}`
+        : `Satellite refresh: ${state.last_notify_count ?? "requested"} connected at ${formatTimestamp(state.last_notify_at)}`);
+    }
+    $("autoAudit").textContent = audit.join(" · ") || "No automatic review has run yet.";
+    syncButtons();
+  }
+
+  async function refreshAutoTrain(populateForm = true) {
+    const data = await api("/api/auto_train", { method: "GET" });
+    renderAutoTrain(data, populateForm);
+    return data;
+  }
+
+  function autoTrainFormPayload() {
+    const payload = {
+      enabled: $("autoEnabled").checked,
+      wake_phrase: ($("autoWakePhrase").value || "").trim(),
+      language: ($("autoLanguage").value || "en").trim(),
+      stt_model: ($("autoSttModel").value || "").trim(),
+      stt_device: $("autoSttDevice").value || "auto",
+      stt_compute_type: $("autoSttComputeType").value || "auto",
+      minimum_transcript_chars: Number($("autoMinimumChars").value || 2),
+      delete_confirmed_wakes: $("autoDeleteConfirmedWakes").checked,
+      promote_close_misses: $("autoPromoteCloseMisses").checked,
+      schedule_hours: Number($("autoScheduleHours").value || 0),
+      minimum_new_negatives: Number($("autoMinimumNegatives").value || 3),
+      advertised_base_url: ($("autoAdvertisedUrl").value || "").trim(),
+      tater_url: ($("autoTaterUrl").value || "").trim(),
+      tater_selector: ($("autoTaterSelector").value || "").trim(),
+      notify_satellites: $("autoNotifySatellites").checked,
+      clear_tater_api_token: $("autoClearTaterToken").checked,
+    };
+    const token = ($("autoTaterToken").value || "").trim();
+    if (token) payload.tater_api_token = token;
+    return payload;
+  }
+
+  async function saveAutoTrain() {
+    uiState.autoBusy = true;
+    syncButtons();
+    setPill($("autoStatus"), "Saving...", "warn");
+    try {
+      const data = await api("/api/auto_train", {
+        method: "PUT",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify(autoTrainFormPayload()),
+      });
+      renderAutoTrain(data, true);
+      setPill($("autoStatus"), data.config?.enabled ? "Saved + enabled" : "Saved + disabled", data.config?.enabled ? "ok" : "");
+      await refreshTrainedWakeWords().catch(() => {});
+      return data;
+    } finally {
+      uiState.autoBusy = false;
+      syncButtons();
+    }
+  }
+
+  async function runAutoTrainAction(action) {
+    uiState.autoBusy = true;
+    syncButtons();
+    setPill($("autoStatus"), "Working...", "warn");
+    try {
+      const data = await api("/api/auto_train/action", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({ action }),
+      });
+      renderAutoTrain(data, false);
+      if (action === "review_now") {
+        setPill($("autoStatus"), `${Number(data.queued || 0)} clip${Number(data.queued || 0) === 1 ? "" : "s"} queued`, "ok");
+      } else if (action === "train_now") {
+        setPill($("autoStatus"), "Training started", "warn");
+        await refreshSession();
+        pollTraining();
+      } else {
+        setPill($("autoStatus"), `Satellite refresh requested${data.count === null || data.count === undefined ? "" : ` for ${data.count}`}`, "ok");
+      }
+      return data;
+    } finally {
+      uiState.autoBusy = false;
+      syncButtons();
+    }
+  }
+
  function captureBadge(item) {
    if (item.blocked_by_vad) return { label: "Blocked by VAD", cls: "warn" };
    const eventType = String(item?.event_type || "").toLowerCase();
@@ -1879,6 +2218,7 @@
      if (item.max_probability !== null && item.max_probability !== undefined) meta.push(`<span class="pill">max ${escapeHtml(item.max_probability)}</span>`);
      if (item.average_probability !== null && item.average_probability !== undefined) meta.push(`<span class="pill">avg ${escapeHtml(item.average_probability)}</span>`);
      if (item.detection_profile) meta.push(`<span class="pill">profile ${escapeHtml(formatDetectionProfile(item.detection_profile))}</span>`);
+      if (item.auto_review_status) meta.push(`<span class="pill ${item.auto_review_status === "error" ? "err" : "warn"}">auto ${escapeHtml(String(item.auto_review_status).replaceAll("_", " "))}</span>`);
      if (item.peak_probability_cutoff !== null && item.peak_probability_cutoff !== undefined) meta.push(`<span class="pill">peak cutoff ${escapeHtml(item.peak_probability_cutoff)}</span>`);
      if (item.probability_cutoff !== null && item.probability_cutoff !== undefined) meta.push(`<span class="pill">avg cutoff ${escapeHtml(item.probability_cutoff)}</span>`);
      if (item.active_window_count !== null && item.active_window_count !== undefined && item.min_active_windows !== null && item.min_active_windows !== undefined) {
@@ -1906,6 +2246,8 @@
            <span class="pill ${badge.cls}">${escapeHtml(badge.label)}</span>
          </div>
          <div class="fileMeta">${meta.join("") || `<span class="muted">No metadata attached</span>`}</div>
+          ${item.transcript ? `<div class="autoAudit"><strong>STT transcript</strong><br>${escapeHtml(item.transcript)}</div>` : ""}
+          ${item.auto_review_error ? `<div class="muted">Auto review error: ${escapeHtml(item.auto_review_error)}</div>` : ""}
          <audio class="audioPlayer" controls preload="none" src="${escapeHtml(item.audio_url || `/api/audio/captured/${encodeURIComponent(item.saved_as)}`)}"></audio>
          <div class="muted">Stored as ${escapeHtml(item.saved_as)} · ${escapeHtml(formatSummary)}</div>
          <div class="captureActions">
@@ -1949,6 +2291,8 @@
    if (item.original_name && item.original_name !== item.saved_as) subtitleParts.push(`From ${item.original_name}`);
    if (when) subtitleParts.push(`Saved ${when}`);
    if (item.message) subtitleParts.push(item.message);
+    if (item.auto_negative) subtitleParts.push("Auto-reviewed false positive");
+    if (item.auto_positive) subtitleParts.push("Auto-promoted close miss");
    let revertBtn = '';
    if (item.trimmed) {
        revertBtn = `<button type="button" data-sample-revert="${escapeAttr(item.saved_as)}" data-bucket="${escapeAttr(bucket)}">Revert</button>`;
@@ -1963,6 +2307,7 @@
          <span class="pill ${badge.cls}">${badge.label}</span>
          ${trimBadgeHtml}
        </div>
+        ${item.transcript ? `<div class="autoAudit"><strong>STT transcript</strong><br>${escapeHtml(item.transcript)}</div>` : ""}
        <audio class="audioPlayer" controls preload="none" src="${escapeAttr(item.audio_url || `/api/audio/${bucket}/${encodeURIComponent(item.saved_as)}`)}?t=${encodeURIComponent(item.created_at || '')}"></audio>
        <div class="muted">Stored in ${bucket === "negative" ? "negative_samples" : "personal_samples"} · ${escapeHtml(formatSummary)}</div>
        <div class="captureActions">
@@ -2250,6 +2595,10 @@
    if (refreshWakeWordsBtn) {
      refreshWakeWordsBtn.disabled = uiState.firmwareBusy;
    }
+    for (const id of ["autoSaveBtn", "autoReviewNowBtn", "autoTrainNowBtn", "autoNotifyNowBtn"]) {
+      const button = $(id);
+      if (button) button.disabled = uiState.autoBusy || (id === "autoTrainNowBtn" && Boolean(training.running));
+    }
  }

  function refreshSessionUI(session) {
@@ -2506,6 +2855,13 @@

  $("phrase").addEventListener("input", syncButtons);
  $("tabTrainer").addEventListener("click", () => setActiveView("trainer"));
+  $("tabAuto").addEventListener("click", () => {
+    setActiveView("auto");
+    refreshAutoTrain(true).catch((error) => {
+      setPill($("autoStatus"), "Status failed", "err");
+      alert("Auto Training status failed: " + error.message);
+    });
+  });
  $("tabCaptured").addEventListener("click", () => setActiveView("captured"));
  $("tabSamples").addEventListener("click", () => {
    setActiveView("samples");
@@ -2556,6 +2912,39 @@
    }
  });

+  $("autoSaveBtn").addEventListener("click", async () => {
+    try {
+      await saveAutoTrain();
+    } catch (error) {
+      setPill($("autoStatus"), "Save failed", "err");
+      alert("Auto Training save failed: " + error.message);
+    }
+  });
+  $("autoReviewNowBtn").addEventListener("click", async () => {
+    try {
+      await runAutoTrainAction("review_now");
+    } catch (error) {
+      setPill($("autoStatus"), "Review failed", "err");
+      alert("Auto review failed: " + error.message);
+    }
+  });
+  $("autoTrainNowBtn").addEventListener("click", async () => {
+    try {
+      await runAutoTrainAction("train_now");
+    } catch (error) {
+      setPill($("autoStatus"), "Training failed", "err");
+      alert("Auto training failed: " + error.message);
+    }
+  });
+  $("autoNotifyNowBtn").addEventListener("click", async () => {
+    try {
+      await runAutoTrainAction("notify_now");
+    } catch (error) {
+      setPill($("autoStatus"), "Refresh failed", "err");
+      alert("Satellite refresh failed: " + error.message);
+    }
+  });
+
  $("openConsoleBtn").addEventListener("click", () => {
    setConsoleLogAutoScroll($("trainLog"), (uiState.training?.log_lines || []).join("\n") || "(no training started)");
    openConsole(true, "Training Console", "Live training output appears here with color-coded console styling.");
@@ -2774,6 +3163,7 @@
      await refreshSession();
      await refreshSamples();
      await refreshCapturedAudio();
+      await refreshAutoTrain(true);
    } catch (_) {}

    try {
@@ -2864,6 +3254,10 @@
  });

  bootstrap();
+  setInterval(() => {
+    if (uiState.activeView !== "auto" || uiState.autoBusy) return;
+    refreshAutoTrain(false).catch(() => {});
+  }, 2500);
 </script>
 </body>
 </html>
--- a/tests/test_auto_train.py
+++ b/tests/test_auto_train.py
@@ -0,0 +1,352 @@
+import io
+import json
+import queue
+import sys
+import tempfile
+import unittest
+import wave
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import Mock, patch
+
+import trainer_server as trainer
+
+
+def silent_wav_bytes(duration_s: float = 0.25) -> bytes:
+    output = io.BytesIO()
+    with wave.open(output, "wb") as wav_file:
+        wav_file.setnchannels(1)
+        wav_file.setsampwidth(2)
+        wav_file.setframerate(16000)
+        wav_file.writeframes(b"\x00\x00" * int(16000 * duration_s))
+    return output.getvalue()
+
+
+class AutoTrainTests(unittest.TestCase):
+    def clear_review_queue(self):
+        while True:
+            try:
+                trainer.AUTO_TRAIN_REVIEW_QUEUE.get_nowait()
+            except queue.Empty:
+                break
+            else:
+                trainer.AUTO_TRAIN_REVIEW_QUEUE.task_done()
+        trainer.AUTO_TRAIN_QUEUED_FILES.clear()
+
+    def setUp(self):
+        self.clear_review_queue()
+        self.tempdir = tempfile.TemporaryDirectory()
+        root = Path(self.tempdir.name)
+        self.original_paths = (
+            trainer.CAPTURED_DIR,
+            trainer.NEGATIVE_DIR,
+            trainer.PERSONAL_DIR,
+            trainer.AUTO_TRAIN_CONFIG_FILE,
+            trainer.AUTO_TRAIN_STATE_FILE,
+        )
+        trainer.CAPTURED_DIR = root / "captured_audio"
+        trainer.NEGATIVE_DIR = root / "negative_samples"
+        trainer.PERSONAL_DIR = root / "personal_samples"
+        trainer.AUTO_TRAIN_CONFIG_FILE = root / "auto_train_config.json"
+        trainer.AUTO_TRAIN_STATE_FILE = root / "auto_train_state.json"
+        for directory in (trainer.CAPTURED_DIR, trainer.NEGATIVE_DIR, trainer.PERSONAL_DIR):
+            directory.mkdir(parents=True)
+
+        self.original_config = dict(trainer.AUTO_TRAIN_CONFIG)
+        self.original_state = dict(trainer.AUTO_TRAIN_STATE)
+        trainer.AUTO_TRAIN_CONFIG.clear()
+        trainer.AUTO_TRAIN_CONFIG.update(
+            trainer._normalize_auto_train_config(
+                {
+                    "enabled": True,
+                    "wake_phrase": "hey tater",
+                    "language": "en",
+                    "tater_url": "http://127.0.0.1:8501",
+                    "stt_device": "auto",
+                    "stt_compute_type": "auto",
+                }
+            )
+        )
+        trainer.AUTO_TRAIN_STATE.clear()
+        trainer.AUTO_TRAIN_STATE.update(trainer.AUTO_TRAIN_DEFAULT_STATE)
+
+    def tearDown(self):
+        (
+            trainer.CAPTURED_DIR,
+            trainer.NEGATIVE_DIR,
+            trainer.PERSONAL_DIR,
+            trainer.AUTO_TRAIN_CONFIG_FILE,
+            trainer.AUTO_TRAIN_STATE_FILE,
+        ) = self.original_paths
+        trainer.AUTO_TRAIN_CONFIG.clear()
+        trainer.AUTO_TRAIN_CONFIG.update(self.original_config)
+        trainer.AUTO_TRAIN_STATE.clear()
+        trainer.AUTO_TRAIN_STATE.update(self.original_state)
+        self.clear_review_queue()
+        self.tempdir.cleanup()
+
+    def add_capture(
+        self,
+        name: str = "wake.wav",
+        wake_word: str = "hey_tater",
+        event_type: str = "wake_detected",
+        blocked_by_vad: bool = False,
+    ) -> Path:
+        audio_path = trainer.CAPTURED_DIR / name
+        audio_path.write_bytes(silent_wav_bytes())
+        trainer._write_sidecar_json(
+            audio_path,
+            {
+                "original_name": name,
+                "wake_word": wake_word,
+                "event_type": event_type,
+                "blocked_by_vad": blocked_by_vad,
+                "review_status": "pending",
+            },
+        )
+        return audio_path
+
+    def test_phrase_matching_normalizes_case_punctuation_and_underscores(self):
+        self.assertTrue(trainer._transcript_contains_wake_phrase("Okay, HEY TATER!", "hey_tater"))
+        self.assertFalse(trainer._transcript_contains_wake_phrase("Turn on the television", "hey tater"))
+
+    def test_phrase_miss_moves_wake_trigger_to_negative_samples(self):
+        self.add_capture()
+        with patch.object(trainer, "_transcribe_capture_with_faster_whisper", return_value="turn on the kitchen lights"):
+            trainer._auto_review_capture("wake.wav")
+
+        self.assertFalse((trainer.CAPTURED_DIR / "wake.wav").exists())
+        negatives = list(trainer.NEGATIVE_DIR.glob("*.wav"))
+        self.assertEqual(len(negatives), 1)
+        metadata = trainer._load_sidecar_json(negatives[0])
+        self.assertTrue(metadata["auto_negative"])
+        self.assertEqual(metadata["review_status"], "auto_approved_negative")
+        self.assertEqual(metadata["transcript"], "turn on the kitchen lights")
+        self.assertEqual(trainer.AUTO_TRAIN_STATE["pending_negative_count"], 1)
+
+    def test_matching_phrase_stays_in_manual_review_inbox(self):
+        audio_path = self.add_capture()
+        with patch.object(trainer, "_transcribe_capture_with_faster_whisper", return_value="hey tater turn on the lights"):
+            trainer._auto_review_capture("wake.wav")
+
+        self.assertTrue(audio_path.exists())
+        self.assertFalse(list(trainer.NEGATIVE_DIR.glob("*.wav")))
+        metadata = trainer._load_sidecar_json(audio_path)
+        self.assertEqual(metadata["auto_review_status"], "wake_phrase_detected")
+        self.assertEqual(trainer.AUTO_TRAIN_STATE["pending_negative_count"], 0)
+
+    def test_matching_phrase_is_deleted_when_cleanup_is_enabled(self):
+        audio_path = self.add_capture()
+        trainer.AUTO_TRAIN_CONFIG["delete_confirmed_wakes"] = True
+        with patch.object(
+            trainer,
+            "_transcribe_capture_with_faster_whisper",
+            return_value="hey tater turn on the lights",
+        ):
+            trainer._auto_review_capture("wake.wav")
+
+        self.assertFalse(audio_path.exists())
+        self.assertFalse(audio_path.with_suffix(".json").exists())
+        self.assertFalse(list(trainer.PERSONAL_DIR.glob("*.wav")))
+        self.assertFalse(list(trainer.NEGATIVE_DIR.glob("*.wav")))
+        self.assertEqual(trainer.AUTO_TRAIN_STATE["last_review_result"], "deleted_confirmed_wake")
+
+    def test_cleanup_processes_previously_confirmed_wake_without_retranscribing(self):
+        audio_path = self.add_capture()
+        metadata = trainer._load_sidecar_json(audio_path)
+        metadata.update(
+            {
+                "auto_review_status": "wake_phrase_detected",
+                "transcript": "hey tater",
+            }
+        )
+        trainer._write_sidecar_json(audio_path, metadata)
+        trainer.AUTO_TRAIN_CONFIG["delete_confirmed_wakes"] = True
+
+        self.assertEqual(trainer._queue_pending_auto_reviews(), 1)
+        with patch.object(trainer, "_transcribe_capture_with_faster_whisper") as transcribe:
+            trainer._auto_review_capture("wake.wav")
+
+        transcribe.assert_not_called()
+        self.assertFalse(audio_path.exists())
+        self.assertEqual(trainer.AUTO_TRAIN_STATE["last_review_transcript"], "hey tater")
+
+    def test_close_miss_is_not_transcribed_by_default(self):
+        audio_path = self.add_capture(event_type="close_miss")
+        with patch.object(trainer, "_transcribe_capture_with_faster_whisper") as transcribe:
+            trainer._auto_review_capture("wake.wav")
+
+        transcribe.assert_not_called()
+        self.assertTrue(audio_path.exists())
+        self.assertFalse(trainer._load_sidecar_json(audio_path).get("auto_review_status"))
+
+    def test_existing_close_miss_is_queued_when_promotion_is_enabled(self):
+        self.add_capture(event_type="close_miss")
+        self.assertEqual(trainer._queue_pending_auto_reviews(), 0)
+
+        trainer.AUTO_TRAIN_CONFIG["promote_close_misses"] = True
+        self.assertEqual(trainer._queue_pending_auto_reviews(), 1)
+
+    def test_close_miss_with_phrase_is_promoted_when_enabled(self):
+        self.add_capture(event_type="close_miss")
+        trainer.AUTO_TRAIN_CONFIG["promote_close_misses"] = True
+        with patch.object(trainer, "_transcribe_capture_with_faster_whisper", return_value="hey tater"):
+            trainer._auto_review_capture("wake.wav")
+
+        self.assertFalse((trainer.CAPTURED_DIR / "wake.wav").exists())
+        positives = list(trainer.PERSONAL_DIR.glob("*.wav"))
+        self.assertEqual(len(positives), 1)
+        metadata = trainer._load_sidecar_json(positives[0])
+        self.assertTrue(metadata["auto_positive"])
+        self.assertEqual(metadata["review_status"], "auto_approved_personal")
+        self.assertEqual(metadata["transcript"], "hey tater")
+        self.assertFalse(list(trainer.NEGATIVE_DIR.glob("*.wav")))
+        self.assertEqual(trainer.AUTO_TRAIN_STATE["pending_negative_count"], 0)
+
+    def test_close_miss_without_phrase_stays_in_inbox(self):
+        audio_path = self.add_capture(event_type="close_miss")
+        trainer.AUTO_TRAIN_CONFIG["promote_close_misses"] = True
+        with patch.object(
+            trainer,
+            "_transcribe_capture_with_faster_whisper",
+            return_value="turn on the lights",
+        ):
+            trainer._auto_review_capture("wake.wav")
+
+        self.assertTrue(audio_path.exists())
+        self.assertFalse(list(trainer.PERSONAL_DIR.glob("*.wav")))
+        self.assertFalse(list(trainer.NEGATIVE_DIR.glob("*.wav")))
+        metadata = trainer._load_sidecar_json(audio_path)
+        self.assertEqual(metadata["auto_review_status"], "close_miss_phrase_not_detected")
+
+    def test_vad_blocked_close_miss_is_never_transcribed(self):
+        audio_path = self.add_capture(event_type="close_miss", blocked_by_vad=True)
+        trainer.AUTO_TRAIN_CONFIG["promote_close_misses"] = True
+        with patch.object(trainer, "_transcribe_capture_with_faster_whisper") as transcribe:
+            trainer._auto_review_capture("wake.wav")
+
+        transcribe.assert_not_called()
+        self.assertTrue(audio_path.exists())
+        self.assertFalse(trainer._load_sidecar_json(audio_path).get("auto_review_status"))
+
+    def test_capture_for_another_wake_word_is_not_transcribed(self):
+        audio_path = self.add_capture(wake_word="computer")
+        with patch.object(trainer, "_transcribe_capture_with_faster_whisper") as transcribe:
+            trainer._auto_review_capture("wake.wav")
+
+        transcribe.assert_not_called()
+        self.assertTrue(audio_path.exists())
+        metadata = trainer._load_sidecar_json(audio_path)
+        self.assertEqual(metadata["auto_review_status"], "different_wake_phrase")
+
+    def test_due_schedule_starts_training_after_minimum_negatives(self):
+        trainer.AUTO_TRAIN_CONFIG["schedule_hours"] = 24
+        trainer.AUTO_TRAIN_CONFIG["minimum_new_negatives"] = 3
+        trainer.AUTO_TRAIN_STATE["pending_negative_count"] = 3
+        trainer.AUTO_TRAIN_STATE["next_run_at"] = "2000-01-01T00:00:00+00:00"
+        with patch.object(trainer, "_start_auto_training", return_value={"ok": True, "started": True}) as start:
+            trainer._maybe_run_scheduled_auto_training()
+
+        start.assert_called_once_with()
+        self.assertTrue(trainer.AUTO_TRAIN_STATE["next_run_at"])
+
+    def test_tater_refresh_repushes_settings_with_selector_and_token(self):
+        trainer.AUTO_TRAIN_CONFIG.update(
+            {
+                "notify_satellites": True,
+                "tater_url": "http://127.0.0.1:8501",
+                "tater_selector": "kitchen-sat",
+                "tater_api_token": "secret-token",
+            }
+        )
+
+        class Response:
+            def __enter__(self):
+                return self
+
+            def __exit__(self, *_args):
+                return False
+
+            def read(self):
+                return b'{"push":{"count":2}}'
+
+        with patch.object(trainer, "urlopen", return_value=Response()) as open_url:
+            result = trainer._notify_tater_satellites()
+
+        self.assertTrue(result["ok"])
+        self.assertEqual(result["count"], 2)
+        request = open_url.call_args.args[0]
+        self.assertEqual(request.full_url, "http://127.0.0.1:8501/api/tater/satellite/v1/settings")
+        self.assertEqual(request.get_header("X-tater-token"), "secret-token")
+        self.assertEqual(json.loads(request.data), {"selector": "kitchen-sat", "settings": {}})
+
+    def test_advertised_url_uses_non_loopback_browser_host(self):
+        request = SimpleNamespace(
+            base_url="http://192.168.1.50:8789/",
+            url=SimpleNamespace(hostname="192.168.1.50", scheme="http", port=8789),
+        )
+        self.assertEqual(trainer._advertised_base_url(request), "http://192.168.1.50:8789")
+
+    def test_advertised_url_replaces_localhost_with_discovered_lan_host(self):
+        request = SimpleNamespace(
+            base_url="http://127.0.0.1:8789/",
+            url=SimpleNamespace(hostname="127.0.0.1", scheme="http", port=8789),
+        )
+        with patch.object(trainer, "_discover_lan_ipv4", return_value="192.168.1.60"):
+            self.assertEqual(trainer._advertised_base_url(request), "http://192.168.1.60:8789")
+
+    def test_configured_public_url_takes_precedence(self):
+        trainer.AUTO_TRAIN_CONFIG["advertised_base_url"] = "http://trainer.local:8789"
+        request = SimpleNamespace(
+            base_url="http://127.0.0.1:8789/",
+            url=SimpleNamespace(hostname="127.0.0.1", scheme="http", port=8789),
+        )
+        self.assertEqual(trainer._advertised_base_url(request), "http://trainer.local:8789")
+
+    def test_faster_whisper_auto_runtime_prefers_cuda_and_float16(self):
+        fake_ctranslate2 = SimpleNamespace(get_cuda_device_count=lambda: 1)
+        with patch.dict(sys.modules, {"ctranslate2": fake_ctranslate2}):
+            self.assertEqual(
+                trainer._resolve_faster_whisper_runtime("auto", "auto"),
+                ("cuda", "float16"),
+            )
+
+    def test_faster_whisper_auto_runtime_falls_back_to_cpu_int8(self):
+        fake_ctranslate2 = SimpleNamespace(get_cuda_device_count=lambda: 0)
+        with patch.dict(sys.modules, {"ctranslate2": fake_ctranslate2}):
+            self.assertEqual(
+                trainer._resolve_faster_whisper_runtime("auto", "auto"),
+                ("cpu", "int8"),
+            )
+
+    def test_faster_whisper_transcription_joins_segments_and_records_runtime(self):
+        fake_model = SimpleNamespace()
+        fake_model.transcribe = Mock(
+            return_value=(
+                iter([SimpleNamespace(text=" turn on "), SimpleNamespace(text="the lights ")]),
+                SimpleNamespace(),
+            )
+        )
+        with (
+            patch.object(trainer, "_resolve_faster_whisper_runtime", return_value=("cuda", "float16")),
+            patch.object(trainer, "_load_faster_whisper_model", return_value=fake_model),
+        ):
+            transcript = trainer._transcribe_capture_with_faster_whisper(
+                Path("wake.wav"),
+                model="small.en",
+                language="en",
+            )
+
+        self.assertEqual(transcript, "turn on the lights")
+        fake_model.transcribe.assert_called_once_with(
+            "wake.wav",
+            language="en",
+            beam_size=1,
+            condition_on_previous_text=False,
+        )
+        self.assertEqual(trainer.AUTO_TRAIN_STATE["last_stt_device"], "cuda")
+        self.assertEqual(trainer.AUTO_TRAIN_STATE["last_stt_compute_type"], "float16")
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/tests/test_calibrate_detector.py
+++ b/tests/test_calibrate_detector.py
@@ -0,0 +1,88 @@
+import importlib.util
+import unittest
+from pathlib import Path
+
+
+SCRIPT_PATH = (
+    Path(__file__).resolve().parents[1]
+    / "cli"
+    / "calibrate_detector.py"
+)
+SPEC = importlib.util.spec_from_file_location("calibrate_detector", SCRIPT_PATH)
+calibrate_detector = importlib.util.module_from_spec(SPEC)
+assert SPEC.loader is not None
+SPEC.loader.exec_module(calibrate_detector)
+
+
+def candidate(cutoff, window, recall, false_accepts_per_hour):
+    return {
+        "probability_cutoff": cutoff,
+        "sliding_window_size": window,
+        "recall": recall,
+        "false_accepts_per_hour": false_accepts_per_hour,
+    }
+
+
+class CalibrationSelectionTests(unittest.TestCase):
+    def test_defaults_are_conservative(self):
+        self.assertEqual(calibrate_detector.DEFAULT_WINDOW_SIZES, [5, 6, 7])
+        self.assertEqual(calibrate_detector.DEFAULT_CUTOFF_MIN, 0.95)
+        self.assertEqual(calibrate_detector.DEFAULT_RECALL_MARGIN, 0.005)
+
+    def test_prefers_zero_false_accepts_within_recall_margin(self):
+        candidates = [
+            candidate(0.95, 5, 0.99894, 0.103408),
+            candidate(0.95, 6, 0.99744, 0.0),
+            candidate(0.95, 7, 0.99554, 0.0),
+        ]
+
+        best, selected_limit = calibrate_detector._select_best_candidate(
+            candidates,
+            target_faph=0.25,
+            recall_margin=0.005,
+        )
+
+        self.assertEqual(best["sliding_window_size"], 6)
+        self.assertEqual(best["false_accepts_per_hour"], 0.0)
+        self.assertEqual(selected_limit, 0.25)
+
+    def test_does_not_trade_away_recall_beyond_margin(self):
+        candidates = [
+            candidate(0.95, 5, 0.99, 0.1),
+            candidate(0.99, 6, 0.90, 0.0),
+        ]
+
+        best, _ = calibrate_detector._select_best_candidate(
+            candidates,
+            target_faph=0.25,
+            recall_margin=0.005,
+        )
+
+        self.assertEqual(best["sliding_window_size"], 5)
+
+    def test_uses_strictest_available_false_accept_tier(self):
+        candidates = [
+            candidate(0.95, 5, 0.99, 0.6),
+            candidate(0.99, 6, 0.99, 1.5),
+        ]
+
+        best, selected_limit = calibrate_detector._select_best_candidate(
+            candidates,
+            target_faph=0.25,
+            recall_margin=0.005,
+        )
+
+        self.assertEqual(best["false_accepts_per_hour"], 0.6)
+        self.assertEqual(selected_limit, 0.75)
+
+    def test_rejects_negative_recall_margin(self):
+        with self.assertRaises(ValueError):
+            calibrate_detector._select_best_candidate(
+                [candidate(0.95, 6, 0.99, 0.0)],
+                target_faph=0.25,
+                recall_margin=-0.001,
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/trainer_server.py
+++ b/trainer_server.py
Author	SHA1	Message	Date
MasterPhooey	5554b2eb5e	Release NVIDIA WakeWord Trainer v13	2026-07-17 20:38:18 -05:00
MasterPhooey	7d77f71dc3	Release NVIDIA WakeWord Trainer v12	2026-07-17 08:21:50 -05:00