Release NVIDIA WakeWord Trainer v14

README.md

@@ -22,7 +22,7 @@ docker pull ghcr.io/tatertotterson/microwakeword:latest
 Tagged releases also publish matching immutable image tags:
 
 ```bash
-docker pull ghcr.io/tatertotterson/microwakeword:v12
+docker pull ghcr.io/tatertotterson/microwakeword:v14
 ```
 
 The release tag must match `VERSION`. Update `WHATS_NEW.md` before tagging; the Docker workflow prepends it to GitHub's automatically generated release notes.
@@ -32,7 +32,7 @@ Python 3.13 TensorFlow build for `sm_120`:
 
 ```bash
 docker pull ghcr.io/tatertotterson/microwakeword:blackwell
-docker pull ghcr.io/tatertotterson/microwakeword:v12-blackwell
+docker pull ghcr.io/tatertotterson/microwakeword:v14-blackwell
 ```
 
 Use the Blackwell image only for RTX 50-series cards. It includes the
@@ -53,9 +53,9 @@ docker run -d \
   ghcr.io/tatertotterson/microwakeword:latest
 ```
 
-Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v12` when you want to pin a known release instead of tracking `latest`.
+Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v14` 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:v12-blackwell`
+or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v14-blackwell`
 in the same `docker run` command.
 
 The flags:
@@ -167,18 +167,25 @@ Starting a new session does not clear samples. Use the clear buttons in `Samples
 
 ## Auto Training
 
-`Auto Training` is an opt-in false-positive loop. It is disabled until you enter the exact wake phrase and enable it.
+`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 positive review.
+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, close misses, VAD-blocked captures, and captures for another wake word stay out of the automatic negative path.
+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.
+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, asks Tater for its connected native satellites, and re-saves each satellite's current wake profile so its JSON tuning and model are fetched 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.
 

VERSION

@@ -1 +1 @@
-12
+14

WHATS_NEW.md

@@ -1,5 +1,2 @@
-- 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; close misses, empty transcripts, and phrase matches remain available for manual review.
-- 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.
+- Fixed automatic and manual satellite refresh so every connected satellite re-fetches its current custom wake JSON profile before reloading the model.
+- The large WHAM augmentation dataset download now reports visible progress in the training log.

cli/setup_wham

@@ -103,7 +103,8 @@ else
     if [ "${actual_filecount}" -eq 0 ] || [ "${actual_filecount}" -ne "${expected_filecount}" ] ; then
         if [ ! -f "${AUDIO_ZIP}" ] ; then
             echo "   Downloading ${AUDIO_ZIPFILE}"
-            curl -sfL "${AUDIO_URL}" -o "${AUDIO_ZIP}"
+            curl -fL --progress-bar "${AUDIO_URL}" -o "${AUDIO_ZIP}" \
+                2> >(tr '\r' '\n' >&2)
         fi
 
         rm -rf "${AUDIO_DIR}" || :

static/index.html

@@ -1288,7 +1288,7 @@
           <div>
             <div class="studioKicker">False-Positive Loop</div>
             <h3>Auto Training</h3>
-            <p>Transcribe real wake triggers, turn confirmed phrase-misses into hard negatives, retrain on your schedule, and ask Tater to refresh connected satellites.</p>
+            <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>
@@ -1305,13 +1305,21 @@
             <span class="studioStepBadge">1</span>
             <div>
               <h3>Review Rules</h3>
-              <p>Only wake-trigger clips are reviewed automatically. Close misses, empty STT results, and clips containing the wake phrase remain in the inbox.</p>
+              <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>New wake triggers will be queued for local Faster Whisper transcription.</span>
+          <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">
@@ -2040,6 +2048,8 @@
       $("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 || "";
@@ -2102,6 +2112,8 @@
       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(),
@@ -2280,6 +2292,7 @@
     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>`;

tests/test_auto_train.py

@@ -1,5 +1,6 @@
 import io
 import json
+import queue
 import sys
 import tempfile
 import unittest
@@ -22,7 +23,18 @@ def silent_wav_bytes(duration_s: float = 0.25) -> bytes:
 
 
 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 = (
@@ -70,9 +82,16 @@ class AutoTrainTests(unittest.TestCase):
         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") -> Path:
+    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(
@@ -80,7 +99,8 @@ class AutoTrainTests(unittest.TestCase):
             {
                 "original_name": name,
                 "wake_word": wake_word,
-                "event_type": "wake_detected",
+                "event_type": event_type,
+                "blocked_by_vad": blocked_by_vad,
                 "review_status": "pending",
             },
         )
@@ -115,6 +135,100 @@ class AutoTrainTests(unittest.TestCase):
         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:
@@ -166,6 +280,63 @@ class AutoTrainTests(unittest.TestCase):
         self.assertEqual(request.get_header("X-tater-token"), "secret-token")
         self.assertEqual(json.loads(request.data), {"selector": "kitchen-sat", "settings": {}})
 
+    def test_tater_refresh_updates_each_connected_satellite_profile(self):
+        trainer.AUTO_TRAIN_CONFIG.update(
+            {
+                "notify_satellites": True,
+                "tater_url": "http://127.0.0.1:8501",
+                "tater_selector": "",
+            }
+        )
+
+        class Response:
+            def __init__(self, payload):
+                self.payload = payload
+
+            def __enter__(self):
+                return self
+
+            def __exit__(self, *_args):
+                return False
+
+            def read(self):
+                return json.dumps(self.payload).encode("utf-8")
+
+        responses = [
+            Response(
+                {
+                    "clients": {
+                        "native:office": {"selector": "native:office", "connected": True},
+                        "native:kitchen": {"connected": True},
+                        "native:garage": {"selector": "native:garage", "connected": False},
+                    }
+                }
+            ),
+            Response({"push": {"count": 1}}),
+            Response({"push": {"count": 1}}),
+        ]
+        with patch.object(trainer, "urlopen", side_effect=responses) as open_url:
+            result = trainer._notify_tater_satellites()
+
+        self.assertTrue(result["ok"])
+        self.assertEqual(result["count"], 2)
+        self.assertEqual(result["selectors"], ["native:office", "native:kitchen"])
+        self.assertEqual(open_url.call_count, 3)
+
+        status_request = open_url.call_args_list[0].args[0]
+        self.assertEqual(status_request.get_method(), "GET")
+        self.assertEqual(status_request.full_url, "http://127.0.0.1:8501/api/tater/satellite/v1/status")
+
+        refresh_requests = [call.args[0] for call in open_url.call_args_list[1:]]
+        self.assertEqual(
+            [json.loads(request.data) for request in refresh_requests],
+            [
+                {"selector": "native:office", "settings": {}},
+                {"selector": "native:kitchen", "settings": {}},
+            ],
+        )
+        self.assertTrue(all(request.get_method() == "POST" for request in refresh_requests))
+
     def test_advertised_url_uses_non_loopback_browser_host(self):
         request = SimpleNamespace(
             base_url="http://192.168.1.50:8789/",

trainer_server.py

@@ -94,6 +94,8 @@ AUTO_TRAIN_DEFAULT_CONFIG: Dict[str, Any] = {
     "stt_device": "auto",
     "stt_compute_type": "auto",
     "minimum_transcript_chars": 2,
+    "delete_confirmed_wakes": False,
+    "promote_close_misses": False,
     "schedule_hours": 24,
     "minimum_new_negatives": 3,
     "advertised_base_url": "",
@@ -473,6 +475,8 @@ def _normalize_auto_train_config(values: Dict[str, Any] | None, *, base: Dict[st
         "stt_device": stt_device,
         "stt_compute_type": stt_compute_type,
         "minimum_transcript_chars": _bounded_int(source.get("minimum_transcript_chars"), 2, 1, 100),
+        "delete_confirmed_wakes": _config_bool(source.get("delete_confirmed_wakes")),
+        "promote_close_misses": _config_bool(source.get("promote_close_misses")),
         "schedule_hours": schedule_hours,
         "minimum_new_negatives": _bounded_int(source.get("minimum_new_negatives"), 3, 1, 10000),
         "advertised_base_url": _normalize_http_base_url(source.get("advertised_base_url")),
@@ -636,12 +640,20 @@ def _transcript_contains_wake_phrase(transcript: Any, wake_phrase: Any) -> bool:
     return f" {normalized_phrase} " in f" {normalized_transcript} "
 
 
-def _captured_event_is_auto_reviewable(metadata: Dict[str, Any]) -> bool:
+def _captured_event_is_close_miss(metadata: Dict[str, Any]) -> bool:
+    event_type = str(metadata.get("event_type") or "captured").strip().lower()
+    return "close" in event_type
+
+
+def _captured_event_is_auto_reviewable(
+    metadata: Dict[str, Any],
+    config: Dict[str, Any] | None = None,
+) -> bool:
     if _parse_bool(metadata.get("blocked_by_vad")):
         return False
     event_type = str(metadata.get("event_type") or "captured").strip().lower()
     if "close" in event_type:
-        return False
+        return bool((config or {}).get("promote_close_misses"))
     return event_type in {"captured", "trigger", "false_trigger"} or "wake" in event_type or "detect" in event_type
 
 
@@ -733,10 +745,14 @@ def _queue_auto_review(file_name: str) -> bool:
 
 def _queue_pending_auto_reviews(*, force: bool = False) -> int:
     queued = 0
+    with AUTO_TRAIN_LOCK:
+        config = dict(AUTO_TRAIN_CONFIG)
+    if not config.get("enabled"):
+        return queued
     CAPTURED_DIR.mkdir(parents=True, exist_ok=True)
     for audio_path in sorted(CAPTURED_DIR.glob("*.wav")):
         metadata = _load_sidecar_json(audio_path)
-        if not _captured_event_is_auto_reviewable(metadata):
+        if not _captured_event_is_auto_reviewable(metadata, config):
             continue
         status = str(metadata.get("auto_review_status") or "").strip()
         if status == "transcribing":
@@ -747,6 +763,12 @@ def _queue_pending_auto_reviews(*, force: bool = False) -> int:
             metadata.pop("auto_review_status", None)
             _write_sidecar_json(audio_path, metadata)
             status = ""
+        if (
+            status == "wake_phrase_detected"
+            and config.get("delete_confirmed_wakes")
+            and not _captured_event_is_close_miss(metadata)
+        ):
+            status = ""
         if status:
             continue
         if _queue_auto_review(audio_path.name):
@@ -782,7 +804,22 @@ def _auto_review_capture(file_name: str) -> None:
         except FileNotFoundError:
             return
         metadata = _load_sidecar_json(audio_path)
-        if metadata.get("auto_review_status") or not _captured_event_is_auto_reviewable(metadata):
+        is_close_miss = _captured_event_is_close_miss(metadata)
+        status = str(metadata.get("auto_review_status") or "").strip()
+        if (
+            status == "wake_phrase_detected"
+            and config.get("delete_confirmed_wakes")
+            and not is_close_miss
+        ):
+            transcript = str(metadata.get("transcript") or "")
+            _remove_audio_with_sidecar(audio_path)
+            _record_auto_review_result(
+                file_name=file_name,
+                transcript=transcript,
+                result="deleted_confirmed_wake",
+            )
+            return
+        if status or not _captured_event_is_auto_reviewable(metadata, config):
             return
         captured_wake_phrase = str(metadata.get("wake_word") or "").strip()
         if captured_wake_phrase and _normalize_transcript_text(captured_wake_phrase) != _normalize_transcript_text(wake_phrase):
@@ -819,12 +856,52 @@ def _auto_review_capture(file_name: str) -> None:
             return
 
         if _transcript_contains_wake_phrase(transcript, wake_phrase):
+            if is_close_miss:
+                metadata["auto_review_status"] = "approved_positive"
+                metadata["auto_review_reason"] = (
+                    "Close miss contained the configured wake phrase and was promoted to a positive sample."
+                )
+                metadata["auto_positive"] = True
+                _write_sidecar_json(audio_path, metadata)
+                _move_captured_audio(
+                    file_name,
+                    PERSONAL_DIR,
+                    target_prefix="sample",
+                    review_status="auto_approved_personal",
+                )
+                _record_auto_review_result(
+                    file_name=file_name,
+                    transcript=transcript,
+                    result="promoted_close_miss",
+                )
+                return
+            if config.get("delete_confirmed_wakes"):
+                _remove_audio_with_sidecar(audio_path)
+                _record_auto_review_result(
+                    file_name=file_name,
+                    transcript=transcript,
+                    result="deleted_confirmed_wake",
+                )
+                return
             metadata["auto_review_status"] = "wake_phrase_detected"
             metadata["auto_review_reason"] = "Wake phrase found in transcript; left for manual positive review."
             _write_sidecar_json(audio_path, metadata)
             _record_auto_review_result(file_name=file_name, transcript=transcript, result="wake_phrase_detected")
             return
 
+        if is_close_miss:
+            metadata["auto_review_status"] = "close_miss_phrase_not_detected"
+            metadata["auto_review_reason"] = (
+                "Close miss did not contain the configured wake phrase; left for manual review."
+            )
+            _write_sidecar_json(audio_path, metadata)
+            _record_auto_review_result(
+                file_name=file_name,
+                transcript=transcript,
+                result="close_miss_phrase_not_detected",
+            )
+            return
+
         metadata["auto_review_status"] = "approved_negative"
         metadata["auto_review_reason"] = "Wake phrase was not found in the STT transcript."
         metadata["auto_negative"] = True
@@ -855,36 +932,86 @@ def _auto_review_capture(file_name: str) -> None:
             AUTO_TRAIN_RUNTIME["review_file"] = ""
 
 
+def _connected_tater_satellite_selectors(payload: Any) -> List[str]:
+    if not isinstance(payload, dict):
+        return []
+
+    clients: Any = None
+    for key in ("clients", "satellites", "devices"):
+        if isinstance(payload.get(key), (dict, list)):
+            clients = payload.get(key)
+            break
+    if isinstance(clients, dict):
+        rows = [
+            (str(key or "").strip(), value)
+            for key, value in clients.items()
+        ]
+    elif isinstance(clients, list):
+        rows = [("", value) for value in clients]
+    else:
+        rows = []
+
+    selectors: List[str] = []
+    seen: set[str] = set()
+    for fallback_selector, row in rows:
+        if not isinstance(row, dict) or not _config_bool(row.get("connected"), False):
+            continue
+        selector = str(row.get("selector") or fallback_selector).strip()
+        if not selector or selector in seen:
+            continue
+        seen.add(selector)
+        selectors.append(selector)
+    return selectors
+
+
 def _notify_tater_satellites() -> Dict[str, Any]:
     with AUTO_TRAIN_LOCK:
         config = dict(AUTO_TRAIN_CONFIG)
     if not config.get("notify_satellites"):
         return {"ok": True, "skipped": True, "message": "Satellite notification is disabled."}
 
-    endpoint = f"{str(config.get('tater_url') or '').rstrip('/')}/api/tater/satellite/v1/settings"
-    body = json.dumps(
-        {
-            "selector": str(config.get("tater_selector") or ""),
-            "settings": {},
-        }
-    ).encode("utf-8")
+    base_url = str(config.get("tater_url") or "").rstrip("/")
+    settings_endpoint = f"{base_url}/api/tater/satellite/v1/settings"
+    status_endpoint = f"{base_url}/api/tater/satellite/v1/status"
     headers = {"Content-Type": "application/json", "User-Agent": "microWakeWord-Trainer/auto-train"}
     token = str(config.get("tater_api_token") or "").strip()
     if token:
         headers["X-Tater-Token"] = token
 
     try:
-        req = URLRequest(endpoint, data=body, headers=headers, method="POST")
-        with urlopen(req, timeout=15) as response:
+        configured_selector = str(config.get("tater_selector") or "").strip()
+        if configured_selector:
+            selectors = [configured_selector]
+        else:
+            status_request = URLRequest(status_endpoint, headers=headers, method="GET")
+            with urlopen(status_request, timeout=15) as response:
+                status_payload = json.loads(response.read().decode("utf-8"))
+            selectors = _connected_tater_satellite_selectors(status_payload)
+
+        count = 0
+        refreshes: List[Dict[str, Any]] = []
+        for selector in selectors:
+            body = json.dumps({"selector": selector, "settings": {}}).encode("utf-8")
+            request = URLRequest(settings_endpoint, data=body, headers=headers, method="POST")
+            with urlopen(request, timeout=15) as response:
                 payload = json.loads(response.read().decode("utf-8"))
             push = payload.get("push") if isinstance(payload, dict) and isinstance(payload.get("push"), dict) else {}
-        count = push.get("count")
+            pushed_count = push.get("count")
+            if isinstance(pushed_count, (int, float)):
+                count += max(0, int(pushed_count))
+            refreshes.append({"selector": selector, "count": pushed_count})
+
         with AUTO_TRAIN_LOCK:
             AUTO_TRAIN_STATE["last_notify_at"] = _iso_now()
             AUTO_TRAIN_STATE["last_notify_count"] = count
             AUTO_TRAIN_STATE["last_notify_error"] = ""
             _save_auto_train_state_locked()
-        return {"ok": True, "count": count, "response": payload}
+        return {
+            "ok": True,
+            "count": count,
+            "selectors": selectors,
+            "refreshes": refreshes,
+        }
     except Exception as exc:
         with AUTO_TRAIN_LOCK:
             AUTO_TRAIN_STATE["last_notify_at"] = _iso_now()
@@ -1719,6 +1846,7 @@ def _sample_item_from_path(audio_path: Path, bucket: str) -> Dict[str, Any]:
         "transcript": meta.get("transcript") or "",
         "transcribed_at": meta.get("transcribed_at") or "",
         "auto_negative": bool(meta.get("auto_negative")),
+        "auto_positive": bool(meta.get("auto_positive")),
         "auto_review_reason": meta.get("auto_review_reason") or "",
         "size_bytes": stat.st_size,
         "audio_url": f"/api/audio/{bucket}/{audio_path.name}",
@@ -2401,8 +2529,8 @@ async def upload_captured_audio(
     }
     _write_sidecar_json(audio_path, sidecar)
     with AUTO_TRAIN_LOCK:
-        auto_review_enabled = bool(AUTO_TRAIN_CONFIG.get("enabled"))
-    if auto_review_enabled and _captured_event_is_auto_reviewable(sidecar):
+        auto_review_config = dict(AUTO_TRAIN_CONFIG)
+    if auto_review_config.get("enabled") and _captured_event_is_auto_reviewable(sidecar, auto_review_config):
         _queue_auto_review(audio_path.name)
 
     return {
@@ -2487,8 +2615,8 @@ async def upload_captured_audio_raw(
     }
     _write_sidecar_json(audio_path, sidecar)
     with AUTO_TRAIN_LOCK:
-        auto_review_enabled = bool(AUTO_TRAIN_CONFIG.get("enabled"))
-    if auto_review_enabled and _captured_event_is_auto_reviewable(sidecar):
+        auto_review_config = dict(AUTO_TRAIN_CONFIG)
+    if auto_review_config.get("enabled") and _captured_event_is_auto_reviewable(sidecar, auto_review_config):
         _queue_auto_review(audio_path.name)
 
     return {