micro-opus fix

Updated image dimensions in README for better display.

README.md

@@ -1,25 +1,15 @@
 <div align="center">
-  <h1>🎙️ microWakeWord Nvidia Trainer & Recorder</h1>
-  <img width="1002" height="593" alt="Screenshot 2026-01-18 at 8 13 35 AM" src="https://github.com/user-attachments/assets/e1411d8a-8638-4df8-992b-09a46c6e5ddc" />
+  <h1>microWakeWord NVIDIA Docker Trainer UI</h1>
+  <img width="800" alt="microWakeWord NVIDIA trainer screenshot" src="https://github.com/user-attachments/assets/694f4cb7-e4d8-4e2b-80ec-b40fb41cbfff" />
 </div>
 
-Train **microWakeWord** detection models using a simple **web-based recorder + trainer UI**, packaged in a Docker container.
+Train custom microWakeWord models in Docker with NVIDIA/CUDA acceleration, generated Piper samples, device-captured samples, reviewed false-wake negatives, live training logs, and ESPHome firmware flashing.
 
-No Jupyter notebooks required. No manual cell execution. Just record your voice (optional) and train.
+Real samples come from device-captured wake audio, close misses, or manual uploads. Every saved sample is normalized to `16 kHz / mono / 16-bit PCM WAV` before training.
 
 ---
 
-<img width="100" height="44" alt="unraid_logo_black-339076895" src="https://github.com/user-attachments/assets/87351bed-3321-4a43-924f-fecf2e4e700f" />
-
-**microWakeWord_Trainer-Nvidia** is available in the **Unraid Community Apps** store.
-Install directly from the Unraid App Store with a one-click template.
-
----
-
-<img width="100" height="56" alt="unraid_logo_black-339076895" src="https://github.com/user-attachments/assets/bf959585-ae13-4b4d-ae62-4202a850d35a" />
-
-
-### Pull the Docker Image
+## Docker Image
 
 ```bash
 docker pull ghcr.io/tatertotterson/microwakeword:latest
@@ -27,143 +17,227 @@ docker pull ghcr.io/tatertotterson/microwakeword:latest
 
 ---
 
-### Run the Container
+## Run The Container
 
 ```bash
 docker run -d \
   --gpus all \
-  -p 8888:8888 \
+  --network host \
+  -e REC_PORT=8789 \
   -v $(pwd):/data \
   ghcr.io/tatertotterson/microwakeword:latest
 ```
 
-**What these flags do:**
-- `--gpus all` → Enables GPU acceleration  
-- `-p 8888:8888` → Exposes the Recorder + Trainer WebUI  
-- `-v $(pwd):/data` → Persists all models, datasets, and cache  
+The flags:
 
----
+- `--gpus all` enables GPU acceleration.
+- `--network host` lets the container receive mDNS/zeroconf traffic for ESPHome auto-detect.
+- `-e REC_PORT=8789` sets the trainer web UI and captured-audio port. Change this value if `8789` is already in use.
+- `-v $(pwd):/data` persists models, downloaded voices, datasets, samples, and firmware caches.
 
-### Open the Recorder WebUI
+Host networking is recommended for the Firmware tab's mDNS device discovery. Manual IP flashing and captured-audio uploads can still work without host networking if the trainer port is reachable, but auto-detect may not see devices from Docker bridge networking.
 
-Open your browser and go to:
-
-👉 **http://localhost:8888**
-
-You’ll see the **microWakeWord Recorder & Trainer UI**.
-
----
-
-## 🎤 Recording Voice Samples (Optional)
-
-Personal voice recordings are **optional**.
-
-- You may **record your own voice** for better accuracy  
-- Or simply **click “Train” without recording anything**
-
-If no recordings are present, training will proceed using **synthetic TTS samples only**.
-
-### Remote systems (important)
-If you are running this on a **remote PC / server**, browser-based recording will not work unless:
-- You use a **reverse proxy** (HTTPS + mic permissions), **or**
-- You access the UI via **localhost** on the same machine
-
-Training itself works fine remotely — only recording requires local microphone access.
-
----
-
-### 🎙️ Recording Flow
-
-1. Enter your wake word
-2. Test pronunciation with **Test TTS**
-3. Choose:
-   - Number of speakers (e.g. family members)
-   - Takes per speaker (default: 10)
-4. Click **Begin recording**
-5. Speak naturally — recording:
-   - Starts when you talk
-   - Stops automatically after silence
-6. Repeat for each speaker
-
-Files are saved automatically to:
-
-```
-personal_samples/
-  speaker01_take01.wav
-  speaker01_take02.wav
-  speaker02_take01.wav
-  ...
-```
-
----
-
-## 🧠 Training Behavior (Important Notes)
-
-### ⏬ First training run
-The **first time you click Train**, the system will download **large training datasets** (background noise, speech corpora, etc.).
-
-- This can take **several minutes**
-- This happens **only once**
-- Data is cached inside `/data`
-
-You **will NOT need to download these again** unless you delete `/data`.
-
----
-
-### 🔁 Re-training is safe and incremental
-
-- You can train **multiple wake words** back-to-back
-- You do **NOT** need to clear any folders between runs
-- Old models are preserved in timestamped output directories
-- All required cleanup and reuse logic is handled automatically
-
----
-
-## 📦 Output Files
-
-When training completes, you’ll get:
-- `<wake_word>.tflite` – quantized streaming model  
-- `<wake_word>.json` – ESPHome-compatible metadata  
-
-Both are saved under:
+Open:
 
 ```text
-/data/output/
+http://localhost:8789
 ```
 
-Each run is placed in its own timestamped folder.
+If you change `REC_PORT`, open that port instead and use the same port in the ESPHome `Trainer App URL`.
 
 ---
 
-## 🎤 Optional: Personal Voice Samples (Advanced)
+## What The UI Does
 
-If you record personal samples:
-- They are automatically augmented
-- They are **up-weighted during training**
-- This significantly improves real-world accuracy
-
-No configuration required — detection is automatic.
+- `Trainer` starts a wake-word session, shows positive/negative sample counts, and launches training.
+- `Captured Audio` reviews clips sent by ESPHome sats, including wake hits, close misses, and false wakes.
+- `Samples` plays, removes, clears, and manually imports personal or negative samples.
+- `Firmware` builds the latest `microWakeWords` ESPHome YAMLs from GitHub and flashes VoicePE or Satellite1 over OTA.
+- Popup consoles show colorized training and firmware logs while long-running jobs are active.
 
 ---
 
-## 🔄 Resetting Everything (Optional)
+## Captured Audio Workflow
 
-If you want a **completely clean slate**:
+To collect samples from a sat, flash it with the Tater firmware from [TaterTotterson/microWakeWords](https://github.com/TaterTotterson/microWakeWords). The `Firmware` tab can build and flash the VoicePE or Satellite1 YAMLs directly from that repo.
 
-Delete the /data folder
+After flashing, the device exposes ESPHome entities for capture setup:
 
-Then restart the container.
+- `Capture Wake Audio` toggles upload of wake-word triggers.
+- `Capture Close Misses` toggles upload of near misses.
+- `Trainer App URL` sets the trainer address, for example `http://<trainer-ip>:8789`.
 
-⚠️ This will:
-- Remove cached datasets
-- Require re-downloading training data
-- Delete trained models
+ESPHome devices can send raw captured audio to:
+
+```text
+/api/upload_captured_audio_raw
+```
+
+Keep the training app running and reachable at the `Trainer App URL` while capture is enabled. The sats upload clips live; if the app is stopped or the URL is wrong, captured audio will not be saved.
+
+In the `Captured Audio` tab:
+
+- play each clip from the inbox
+- mark good wake-word clips as `This is good`
+- mark bad triggers as `False wake`
+- discard clips that should not be used
+
+Approved clips move into:
+
+```text
+/data/personal_samples/
+```
+
+False wakes move into:
+
+```text
+/data/negative_samples/
+```
+
+Captured audio is boosted for easier playback in the UI, then kept in the correct training format.
 
 ---
 
-## 🙌 Credits
+## Samples
 
-Built on top of the excellent  
-**https://github.com/kahrendt/microWakeWord**
+The `Samples` tab is the sample library.
 
-Huge thanks to the original authors ❤️
+- `Personal` samples are positive examples of the wake word.
+- `Negative` samples are reviewed false wakes or hard negatives.
+- Both can be played back and removed one at a time.
+- Manual upload is available here as an optional seed path.
+
+Accepted manual upload formats include:
+
+- WAV
+- MP3
+- M4A
+- FLAC
+- OGG
+- AAC
+- OPUS
+- WEBM
+
+Uploads are validated or converted with `ffmpeg` into:
+
+```text
+16 kHz / mono / 16-bit PCM WAV
+```
+
+Starting a new session does not clear samples. Use the clear buttons in `Samples` if you want to remove saved personal or negative clips.
+
+---
+
+## Training Flow
+
+1. Enter the wake phrase in `Trainer`.
+2. Choose the language.
+3. Optionally test pronunciation with `Test TTS`.
+4. Review the positive and negative sample counts.
+5. Click `Start training`.
+6. Watch the popup training console.
+
+Personal samples are optional. Training can run with zero personal samples after confirmation, using generated TTS samples and the stock negative datasets.
+
+Reviewed negative samples are converted into `/data/work/reviewed_negative_features/` and inserted into the training YAML as a hard-negative feature set when present.
+
+---
+
+## Language Support
+
+The language picker is dynamic.
+
+- `en` is always available.
+- English keeps the existing dedicated generator model path.
+- Non-English languages are discovered from the Piper voices catalog and any local Piper voice metadata.
+- When a non-English language is selected, the trainer downloads all voices for that selected language only.
+- Already-downloaded voices are reused.
+- It does not download every language up front.
+
+If the upstream Piper catalog is unavailable, already-installed local voices are used when available.
+
+---
+
+## Dataset Behavior
+
+The first training run downloads and prepares missing training assets into `/data`, including:
+
+- Piper voices for the selected language
+- negative datasets and background data
+- the Python training environment
+- generated samples and augmented feature caches
+
+After those assets are prepared, later runs reuse the local copies unless the mounted `/data` contents are deleted.
+
+---
+
+## Firmware Flashing
+
+The `Firmware` tab builds and flashes Tater firmware for supported ESPHome sats.
+
+- Downloads the latest firmware YAML templates from `TaterTotterson/microWakeWords` on GitHub.
+- Lets you choose `VoicePE` or `Satellite1`.
+- Auto-detects ESPHome devices with mDNS when the container is running with host networking.
+- Allows manual IP or hostname entry if discovery does not find the device.
+- Saves firmware form values so you do not re-enter sounds and URLs every run.
+- Lists locally trained wake words from `/data/trained_wake_words/` for easy model selection.
+- Builds with ESPHome and flashes OTA.
+- Streams ESPHome output in a colorized firmware console.
+
+Firmware YAMLs are intentionally pulled from GitHub each time. There is no local fallback path in the trainer UI.
+
+---
+
+## Output Files
+
+Successful runs produce timestamped training output folders such as:
+
+```text
+/data/output/<timestamp>-<wake_word>-<samples>-<steps>/<wake_word>.tflite
+/data/output/<timestamp>-<wake_word>-<samples>-<steps>/<wake_word>.json
+```
+
+The trainer also syncs firmware-ready artifacts into:
+
+```text
+/data/trained_wake_words/<wake_word>.tflite
+/data/trained_wake_words/<wake_word>.json
+```
+
+The firmware tab uses `/data/trained_wake_words/` to populate the wake-word dropdown.
+
+---
+
+## Resetting Everything
+
+If you want a clean slate, stop the container and remove the contents of the mounted `/data` directory.
+
+That removes:
+
+- personal samples
+- negative samples
+- captured inbox clips
+- downloaded Piper voices
+- cached datasets
+- training environments
+- trained models
+- firmware build caches
+
+---
+
+## Important Notes
+
+- Personal samples are optional.
+- Negative samples are optional but useful for reducing false wakes.
+- The UI server is `trainer_server.py`.
+- The launcher is `run.sh`.
+- Firmware capture settings live on the ESPHome device and can be toggled from the device entities after flashing.
+
+---
+
+## Credits
+
+Built on top of:
+
+- [microWakeWord](https://github.com/kahrendt/microWakeWord)
+- [piper-sample-generator](https://github.com/rhasspy/piper-sample-generator)

cli/calibrate_detector.py

@@ -0,0 +1,405 @@
+#!/usr/bin/env python3
+"""Choose detector metadata that better matches the trained model."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Iterable, Sequence
+
+import numpy as np
+import yaml
+
+from microwakeword.data import FeatureHandler
+from microwakeword.inference import Model
+
+
+DEFAULT_WINDOW_SIZES = [3, 4, 5, 6, 7]
+DEFAULT_TARGET_FAPH = float(os.environ.get("MWW_CALIBRATION_TARGET_FAPH", "1.0"))
+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", "0.00"))
+DEFAULT_CUTOFF_MAX = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MAX", "1.00"))
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Calibrate microWakeWord detector metadata from validation data."
+    )
+    parser.add_argument(
+        "--training-config",
+        default="trained_models/wakeword/training_config.yaml",
+        help="Path to the saved microWakeWord training_config.yaml file.",
+    )
+    parser.add_argument(
+        "--model",
+        default=(
+            "trained_models/wakeword/tflite_stream_state_internal_quant/"
+            "stream_state_internal_quant.tflite"
+        ),
+        help="Path to the quantized streaming TFLite model.",
+    )
+    parser.add_argument(
+        "--output",
+        default=(
+            "trained_models/wakeword/tflite_stream_state_internal_quant/"
+            "detection_calibration.json"
+        ),
+        help="Where to write the selected detector settings as JSON.",
+    )
+    parser.add_argument(
+        "--window-sizes",
+        default=",".join(str(value) for value in DEFAULT_WINDOW_SIZES),
+        help="Comma-separated sliding window sizes to evaluate.",
+    )
+    parser.add_argument(
+        "--target-faph",
+        type=float,
+        default=DEFAULT_TARGET_FAPH,
+        help="Target ambient false accepts per hour for the selected operating point.",
+    )
+    parser.add_argument(
+        "--cooldown-slices",
+        type=int,
+        default=DEFAULT_COOLDOWN_SLICES,
+        help="Cooldown slices to use when estimating false accepts per hour.",
+    )
+    parser.add_argument(
+        "--positive-skip-slices",
+        type=int,
+        default=DEFAULT_POSITIVE_SKIP_SLICES,
+        help="Initial streaming slices to ignore when scoring positive examples.",
+    )
+    parser.add_argument(
+        "--cutoff-step",
+        type=float,
+        default=DEFAULT_CUTOFF_STEP,
+        help="Cutoff increment to evaluate between cutoff-min and cutoff-max.",
+    )
+    parser.add_argument(
+        "--cutoff-min",
+        type=float,
+        default=DEFAULT_CUTOFF_MIN,
+        help="Minimum cutoff to evaluate.",
+    )
+    parser.add_argument(
+        "--cutoff-max",
+        type=float,
+        default=DEFAULT_CUTOFF_MAX,
+        help="Maximum cutoff to evaluate.",
+    )
+    return parser.parse_args()
+
+
+def _parse_window_sizes(raw: str) -> list[int]:
+    values = []
+    for item in (raw or "").split(","):
+        item = item.strip()
+        if not item:
+            continue
+        value = int(item)
+        if value < 1:
+            raise ValueError("window sizes must be >= 1")
+        values.append(value)
+    if not values:
+        raise ValueError("at least one window size is required")
+    return sorted(set(values))
+
+
+def _moving_average(values: Sequence[float], window_size: int) -> np.ndarray:
+    array = np.asarray(values, dtype=np.float32)
+    if array.size == 0:
+        return array
+    if window_size <= 1:
+        return array
+    if array.size < window_size:
+        return np.asarray([float(array.mean())], dtype=np.float32)
+    cumsum = np.cumsum(np.insert(array, 0, 0.0))
+    averaged = (cumsum[window_size:] - cumsum[:-window_size]) / float(window_size)
+    return averaged.astype(np.float32)
+
+
+def _compute_false_accepts_per_hour(
+    probabilities_per_track: Iterable[np.ndarray],
+    cutoffs: np.ndarray,
+    cooldown_slices: int,
+    stride: int,
+    step_seconds: float,
+) -> tuple[np.ndarray, float]:
+    cutoffs = np.asarray(cutoffs, dtype=np.float32)
+    false_accepts = np.zeros(cutoffs.shape[0], dtype=np.float64)
+    duration_hours = 0.0
+
+    for track_probabilities in probabilities_per_track:
+        if track_probabilities.size == 0:
+            continue
+        duration_hours += (
+            len(track_probabilities) * stride * step_seconds / 3600.0
+        )
+        cooldown = np.full(cutoffs.shape[0], cooldown_slices, dtype=np.int32)
+        for probability in track_probabilities:
+            cooldown = np.maximum(cooldown - 1, 0)
+            accepted = (cooldown == 0) & (probability > cutoffs)
+            false_accepts += accepted.astype(np.float64)
+            cooldown = np.where(accepted, cooldown_slices, cooldown)
+
+    if duration_hours <= 0:
+        return np.full(cutoffs.shape[0], math.inf, dtype=np.float64), 0.0
+
+    return false_accepts / duration_hours, duration_hours
+
+
+def _select_best_candidate(
+    candidates: list[dict[str, float]],
+    target_faph: float,
+) -> tuple[dict[str, float], float]:
+    fallback_limits = [
+        target_faph,
+        max(target_faph * 2.0, target_faph + 0.5),
+        max(target_faph * 4.0, 2.0),
+    ]
+
+    def tier(candidate: dict[str, float]) -> int:
+        for index, limit in enumerate(fallback_limits):
+            if candidate["false_accepts_per_hour"] <= limit + 1e-9:
+                return index
+        return len(fallback_limits)
+
+    best = min(
+        candidates,
+        key=lambda candidate: (
+            tier(candidate),
+            -candidate["recall"],
+            candidate["false_accepts_per_hour"],
+            abs(candidate["sliding_window_size"] - 5),
+            -candidate["probability_cutoff"],
+        ),
+    )
+
+    tier_index = tier(best)
+    if tier_index < len(fallback_limits):
+        return best, fallback_limits[tier_index]
+    return best, float("inf")
+
+
+def _load_config(config_path: Path) -> dict:
+    with config_path.open("r", encoding="utf-8") as handle:
+        return yaml.load(handle.read(), Loader=yaml.Loader)
+
+
+def _load_eval_sets(
+    handler: FeatureHandler,
+    config: dict,
+) -> tuple[str, str, list[np.ndarray], list[np.ndarray]]:
+    for positive_mode, ambient_mode in (
+        ("validation", "validation_ambient"),
+        ("testing", "testing_ambient"),
+    ):
+        positive_tracks, labels, _ = handler.get_data(
+            positive_mode,
+            batch_size=config["batch_size"],
+            features_length=config["spectrogram_length"],
+            truncation_strategy="none",
+        )
+        ambient_tracks, _, _ = handler.get_data(
+            ambient_mode,
+            batch_size=config["batch_size"],
+            features_length=config["spectrogram_length"],
+            truncation_strategy="none",
+        )
+        positives = [
+            np.asarray(track)
+            for track, label in zip(positive_tracks, labels)
+            if bool(label)
+        ]
+        ambient = [np.asarray(track) for track in ambient_tracks]
+        if positives and ambient:
+            return positive_mode, ambient_mode, positives, ambient
+    raise RuntimeError(
+        "No suitable validation/testing data was found for detector calibration."
+    )
+
+
+def _predict_tracks(
+    model: Model,
+    tracks: Sequence[np.ndarray],
+    label: str,
+) -> list[np.ndarray]:
+    predictions: list[np.ndarray] = []
+    total = len(tracks)
+    print(f"→ Running streaming inference on {total} {label} track(s)")
+    for index, track in enumerate(tracks, start=1):
+        values = np.asarray(model.predict_spectrogram(track), dtype=np.float32)
+        predictions.append(values)
+        if index == total or index % 25 == 0:
+            print(f"   {label}: {index}/{total}")
+    return predictions
+
+
+def main() -> int:
+    args = parse_args()
+    window_sizes = _parse_window_sizes(args.window_sizes)
+    if args.cutoff_step <= 0:
+        raise ValueError("cutoff-step must be > 0")
+    if args.cutoff_max < args.cutoff_min:
+        raise ValueError("cutoff-max must be >= cutoff-min")
+
+    config_path = Path(args.training_config)
+    model_path = Path(args.model)
+    output_path = Path(args.output)
+
+    if not config_path.exists():
+        raise FileNotFoundError(f"Training config not found: {config_path}")
+    if not model_path.exists():
+        raise FileNotFoundError(f"Streaming TFLite model not found: {model_path}")
+
+    cutoffs = np.arange(
+        args.cutoff_min,
+        args.cutoff_max + (args.cutoff_step / 2.0),
+        args.cutoff_step,
+        dtype=np.float32,
+    )
+    cutoffs = np.clip(cutoffs, 0.0, 1.0)
+    cutoffs = np.unique(np.round(cutoffs, 4))
+
+    print("===== Detector Calibration =====")
+    print(f"→ Model: {model_path}")
+    print(f"→ Training config: {config_path}")
+    print(
+        f"→ Evaluating window sizes {window_sizes} with target <= "
+        f"{args.target_faph:.2f} false accepts/hour"
+    )
+
+    config = _load_config(config_path)
+    config["flags"] = config.get("flags", {})
+    handler = FeatureHandler(config)
+
+    positive_mode, ambient_mode, positive_tracks, ambient_tracks = _load_eval_sets(
+        handler, config
+    )
+
+    print(
+        f"→ Using {positive_mode} positives ({len(positive_tracks)}) and "
+        f"{ambient_mode} ambient tracks ({len(ambient_tracks)})"
+    )
+
+    model = Model(str(model_path), stride=config["stride"])
+    positive_predictions = _predict_tracks(model, positive_tracks, "positive")
+    ambient_predictions = _predict_tracks(model, ambient_tracks, "ambient")
+
+    candidates: list[dict[str, float]] = []
+    best_by_window: list[dict[str, float]] = []
+    step_seconds = config["window_step_ms"] / 1000.0
+
+    for window_size in window_sizes:
+        ambient_averages = [
+            _moving_average(track, window_size) for track in ambient_predictions
+        ]
+        positive_maxima = []
+        for track in positive_predictions:
+            search = (
+                track[args.positive_skip_slices :]
+                if track.size > args.positive_skip_slices
+                else track
+            )
+            averaged = _moving_average(search, window_size)
+            if averaged.size == 0:
+                averaged = _moving_average(track, window_size)
+            positive_maxima.append(float(np.max(averaged)) if averaged.size else 0.0)
+
+        positive_maxima_array = np.asarray(positive_maxima, dtype=np.float32)
+        recall_by_cutoff = np.mean(
+            positive_maxima_array[None, :] > cutoffs[:, None], axis=1
+        )
+        faph_by_cutoff, ambient_hours = _compute_false_accepts_per_hour(
+            ambient_averages,
+            cutoffs,
+            args.cooldown_slices,
+            stride=config["stride"],
+            step_seconds=step_seconds,
+        )
+
+        window_candidates = []
+        for cutoff, recall, faph in zip(cutoffs, recall_by_cutoff, faph_by_cutoff):
+            candidate = {
+                "probability_cutoff": float(round(float(cutoff), 2)),
+                "sliding_window_size": int(window_size),
+                "recall": float(recall),
+                "false_accepts_per_hour": float(faph),
+                "ambient_hours": float(ambient_hours),
+            }
+            candidates.append(candidate)
+            window_candidates.append(candidate)
+
+        best_window, _ = _select_best_candidate(window_candidates, args.target_faph)
+        best_by_window.append(best_window)
+        print(
+            "   window={window}: cutoff={cutoff:.2f}; recall={recall:.2%}; "
+            "ambient_faph={faph:.3f}".format(
+                window=window_size,
+                cutoff=best_window["probability_cutoff"],
+                recall=best_window["recall"],
+                faph=best_window["false_accepts_per_hour"],
+            )
+        )
+
+    best, selected_limit = _select_best_candidate(candidates, args.target_faph)
+    if best["false_accepts_per_hour"] > args.target_faph + 1e-9:
+        print(
+            "⚠️  No candidate met the target false accepts/hour budget; "
+            "using the best fallback operating point."
+        )
+
+    print(
+        "✓ Selected cutoff={cutoff:.2f}, window={window}, recall={recall:.2%}, "
+        "ambient_faph={faph:.3f}".format(
+            cutoff=best["probability_cutoff"],
+            window=best["sliding_window_size"],
+            recall=best["recall"],
+            faph=best["false_accepts_per_hour"],
+        )
+    )
+
+    output = {
+        "probability_cutoff": best["probability_cutoff"],
+        "sliding_window_size": best["sliding_window_size"],
+        "target_false_accepts_per_hour": float(args.target_faph),
+        "selected_false_accepts_per_hour_limit": (
+            None if math.isinf(selected_limit) else float(selected_limit)
+        ),
+        "selected_metrics": {
+            "recall": round(best["recall"], 6),
+            "false_accepts_per_hour": round(best["false_accepts_per_hour"], 6),
+            "ambient_hours": round(best["ambient_hours"], 6),
+        },
+        "evaluation": {
+            "positive_dataset": positive_mode,
+            "ambient_dataset": ambient_mode,
+            "positive_tracks": len(positive_tracks),
+            "ambient_tracks": len(ambient_tracks),
+            "cooldown_slices": int(args.cooldown_slices),
+            "positive_skip_slices": int(args.positive_skip_slices),
+            "window_sizes": window_sizes,
+            "cutoff_min": round(float(cutoffs[0]), 4),
+            "cutoff_max": round(float(cutoffs[-1]), 4),
+            "cutoff_step": float(args.cutoff_step),
+        },
+        "per_window_best": best_by_window,
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+    }
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8")
+    print(f"📝 Wrote calibration to {output_path}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

cli/setup_audioset

@@ -130,6 +130,73 @@ print(f"   AudioSet complete ({ok} ok, {skipped} skipped, {len(audioset_bad)} fa
 EOF
 }
 
+converter_from_dataset_api() {
+    # shellcheck source=/dev/null
+    source "${DATA_DIR}/.venv/bin/activate"
+
+    python - "${AUDIO16K_DIR}" <<-'EOF'
+import sys
+from pathlib import Path
+
+import librosa
+import numpy as np
+import scipy.io.wavfile
+from datasets import load_dataset
+
+def write_wav(dst: Path, data: np.ndarray, sr: int):
+    dst.parent.mkdir(parents=True, exist_ok=True)
+    x = np.clip(data, -1.0, 1.0)
+    scipy.io.wavfile.write(dst, sr, (x * 32767).astype(np.int16))
+
+audioset_out = Path(sys.argv[1])
+
+print("   AudioSet FLAC tarballs are unavailable; using Hugging Face datasets API instead.")
+dataset = load_dataset(
+    "agkphysics/AudioSet",
+    "balanced",
+    split="train",
+    streaming=True,
+)
+
+audioset_bad = []
+ok = 0
+skipped = 0
+heartbeat_every = 250
+
+for idx, sample in enumerate(dataset, start=1):
+    try:
+        video_id = str(sample.get("video_id") or f"audioset_{idx:06d}")
+        outfile = audioset_out / f"{video_id}.wav"
+        if outfile.exists():
+            skipped += 1
+            continue
+
+        audio = sample.get("audio") or {}
+        y = np.asarray(audio.get("array"))
+        sr = int(audio.get("sampling_rate") or 0)
+        if y.size == 0 or sr <= 0:
+            raise ValueError("missing decoded audio")
+        if y.ndim > 1:
+            y = np.mean(y, axis=-1)
+        if sr != 16000:
+            y = librosa.resample(y.astype(np.float32), orig_sr=sr, target_sr=16000)
+        if y.size == 0:
+            raise ValueError("empty audio")
+        write_wav(outfile, y, 16000)
+        ok += 1
+    except Exception as exc:
+        audioset_bad.append(f"{sample.get('video_id', idx)}:{exc}")
+
+    if idx == 1 or (idx % heartbeat_every) == 0:
+        print(f"   AudioSet API progress: {idx} clips processed (ok={ok}, skipped={skipped}, failed={len(audioset_bad)})")
+
+if audioset_bad:
+    (audioset_out / "audioset_corrupted_files.log").write_text("\n".join(audioset_bad))
+
+print(f"   AudioSet complete via datasets API ({ok} ok, {skipped} skipped, {len(audioset_bad)} failed)")
+EOF
+}
+
 expected_filecount=$(get_total_filecount filecounts)
 actual_filecount=$(find "${AUDIO16K_DIR}" -name "*.wav" 2>/dev/null | wc -l) || :
 write_filecount=false
@@ -139,7 +206,10 @@ if [ "${actual_filecount}" -ne 0 ] ; then
     echo "   Existing ${AUDIO16K_DIR} present (${actual_filecount} wav); skipping extract/convert"
 else
     dl=$(find_rev)
-    [ -n "$dl" ] || { echo "   Could not locate an AudioSet revision with FLAC tarballs still present on HF." ; exit 1 ; }
+    if [ -z "$dl" ] ; then
+        rm -rf "${AUDIO16K_DIR}/audioset_corrupted_files.log" || :
+        converter_from_dataset_api
+    else
         rev=${dl%%,*}
         pattern=${dl##*,}
 
@@ -174,6 +244,7 @@ else
             echo "   Converted file count(${actual_filecount}) != expected file count(${expected_filecount})" >&2
             echo "   WARNING: mismatch is expected if some AudioSet files are corrupted; continuing." >&2
         fi
+    fi
 fi
 
 if ${write_filecount} ; then

cli/setup_fma

@@ -27,8 +27,11 @@ cd "${DATA_DIR}/training_datasets"
 
 echo "***** Checking FMA *****"
 
-AUDIO_URL="https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/fma_xs.zip"
-AUDIO_ZIPFILE="fma_xs.zip"
+AUDIO_URLS=(
+    "https://os.unil.cloud.switch.ch/fma/fma_small.zip"
+    "https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/fma_xs.zip"
+)
+AUDIO_ZIPFILE="fma_small.zip"
 AUDIO_ZIP="./downloads/${AUDIO_ZIPFILE}"
 AUDIO_DIR="fma"
 mkdir -p "${AUDIO_DIR}" || :
@@ -81,6 +84,52 @@ EOF
 
 }
 
+extract_zip_with_python() {
+    local zip_path="$1"
+    local dest_dir="$2"
+
+    "${DATA_DIR}/.venv/bin/python" - "${zip_path}" "${dest_dir}" <<-'EOF'
+import sys
+import zipfile
+from pathlib import Path
+from tqdm import tqdm
+
+zip_path = Path(sys.argv[1])
+dest_dir = Path(sys.argv[2])
+
+if (not zip_path.exists()) or zip_path.stat().st_size == 0:
+    raise SystemExit(f"Archive missing or empty: {zip_path}")
+
+with zipfile.ZipFile(zip_path, "r") as zf:
+    members = zf.infolist()
+    size_gb = zip_path.stat().st_size / (1024 ** 3)
+    print(f"   Extracting {zip_path.name} ({len(members)} entries, {size_gb:.1f} GiB)...")
+    for member in tqdm(members, desc="   FMA zip extract", unit="file"):
+        zf.extract(member, dest_dir)
+EOF
+}
+
+download_with_fallbacks() {
+    local output="$1"
+    shift
+    local urls=( "$@" )
+    local rc=1
+
+    for url in "${urls[@]}" ; do
+        for attempt in 1 2 3 4 ; do
+            curl -sfL "${url}" -o "${output}" && [ -s "${output}" ] && return 0
+            rc=$?
+            rm -f "${output}" || :
+            if [ "${attempt}" -lt 4 ] ; then
+                echo "   Retry ${attempt}/3 after download failure"
+                sleep $(( attempt * 2 ))
+            fi
+        done
+    done
+
+    return "${rc}"
+}
+
 expected_filecount=${filecounts[${AUDIO_ZIPFILE}]}
 actual_filecount=$(find ${AUDIO16K_DIR} -name '*.wav' 2>/dev/null | wc -l) || :
 write_filecount=false
@@ -92,13 +141,16 @@ else
     if [ "${actual_filecount}" -eq 0 ] || [ "${actual_filecount}" -ne "${expected_filecount}" ] ; then
         if [ ! -f "${AUDIO_ZIP}" ]  ; then
             echo "   Downloading ${AUDIO_ZIPFILE}"
-            curl -sfL "${AUDIO_URL}" -o "${AUDIO_ZIP}"
+            download_with_fallbacks "${AUDIO_ZIP}" "${AUDIO_URLS[@]}" || {
+                echo "   Failed to download ${AUDIO_ZIPFILE} from all configured sources." >&2
+                exit 1
+            }
         fi
 
         rm -rf "${AUDIO_DIR}" || :
         mkdir "${AUDIO_DIR}"
-        echo "   Unzipping ${AUDIO_ZIPFILE}"
-        unzip -q -d "${AUDIO_DIR}" "${AUDIO_ZIP}"
+        echo "   Extracting ${AUDIO_ZIPFILE}"
+        extract_zip_with_python "${AUDIO_ZIP}" "${AUDIO_DIR}"
     fi
     if "${CLEANUP_ARCHIVES}" && [ -f "${AUDIO_ZIP}" ] ; then
         echo "   Cleaning up ${AUDIO_ZIPFILE}"
@@ -128,4 +180,3 @@ fi
 
 echo "   FMA complete"
 exit 0
-

cli/setup_python_venv

@@ -242,29 +242,7 @@ if [ ! -s "${MODEL_FILE}.json" ] ; then
     curl -sfL "${MODEL_URL}.json" -o "${MODEL_FILE}.json"
 fi
 
-# --- Dutch ONNX voices (single-speaker, used with --language=nl) ---
-# Working Dutch voices: pim, ronnie (nl_NL) and nathalie (nl_BE).
-# nl_NL-mls-medium is intentionally excluded (known Piper issue: outputs gibberish).
-HF_VOICES="https://huggingface.co/rhasspy/piper-voices/resolve/main"
-declare -a NL_VOICES=(
-    "nl/nl_NL/pim/medium/nl_NL-pim-medium"
-    "nl/nl_NL/ronnie/medium/nl_NL-ronnie-medium"
-    "nl/nl_BE/nathalie/medium/nl_BE-nathalie-medium"
-)
-echo "   ===== Checking Dutch Piper voices ====="
-for voice_path in "${NL_VOICES[@]}" ; do
-    voice_name="$(basename "${voice_path}")"
-    onnx_file="${VOICES_DIR}/${voice_name}.onnx"
-    json_file="${VOICES_DIR}/${voice_name}.onnx.json"
-    if [ ! -f "${onnx_file}" ] ; then
-        echo "      Downloading ${voice_name}.onnx"
-        curl -sfL "${HF_VOICES}/${voice_path}.onnx?download=true" -o "${onnx_file}"
-    fi
-    if [ ! -f "${json_file}" ] ; then
-        echo "      Downloading ${voice_name}.onnx.json"
-        curl -sfL "${HF_VOICES}/${voice_path}.onnx.json?download=true" -o "${json_file}"
-    fi
-done
+echo "   Non-English Piper voices will be downloaded on demand for the selected language."
 
 ${GPU} && onnxgpu='-gpu[cuda]' || onnxgpu=""
 echo "   ===== Installing onnxruntime${onnxgpu} ====="

cli/wake_word_sample_augmenter

@@ -18,6 +18,8 @@ parser.add_argument("--output-dir", type=str, help="Wake word output dir. Defaul
 # Personal inputs/outputs (NEW)
 parser.add_argument("--personal-dir", type=str, help="Personal WAV dir. Default: <data-dir>/personal_samples", required=False)
 parser.add_argument("--personal-output-dir", type=str, help="Personal features output dir. Default: <data-dir>/work/personal_augmented_features", required=False)
+parser.add_argument("--negative-dir", type=str, help="Reviewed negative WAV dir. Default: <data-dir>/negative_samples", required=False)
+parser.add_argument("--negative-output-dir", type=str, help="Reviewed negative features output dir. Default: <data-dir>/work/reviewed_negative_features", required=False)
 
 # Dataset dirs
 parser.add_argument("--mit-rirs-16k-dir", type=str, help="MIT RIR input directory. Default: <data-dir>/training_datasets/mit_rirs_16k", required=False)
@@ -57,6 +59,17 @@ if not args.personal_output_dir:
 else:
     args.personal_output_dir = os.path.realpath(args.personal_output_dir)
 
+# Reviewed negative defaults
+if not args.negative_dir:
+    args.negative_dir = os.path.join(args.data_dir, "negative_samples")
+else:
+    args.negative_dir = os.path.realpath(args.negative_dir)
+
+if not args.negative_output_dir:
+    args.negative_output_dir = os.path.join(work_dir, "reviewed_negative_features")
+else:
+    args.negative_output_dir = os.path.realpath(args.negative_output_dir)
+
 # Dataset defaults
 if not args.mit_rirs_16k_dir:
     args.mit_rirs_16k_dir = os.path.join(args.data_dir, "training_datasets", "mit_rirs_16k")
@@ -205,7 +218,7 @@ def bind_wav_generator(clips_obj: Clips, wav_dir: str):
 
     clips_obj.audio_generator = types.MethodType(audio_generator_from_wavs, clips_obj)
 
-def generate_feature_set(input_wav_dir: str, out_root_dir: str, label: str):
+def generate_feature_set(input_wav_dir: str, out_root_dir: str, label: str, *, remove_silence: bool = True):
     files = glob.glob(os.path.join(input_wav_dir, "*.wav"))
     if not files:
         print(f"ℹ️  No WAVs found for {label} in: {input_wav_dir} (skipping)")
@@ -218,7 +231,7 @@ def generate_feature_set(input_wav_dir: str, out_root_dir: str, label: str):
         input_directory=input_wav_dir,
         file_pattern="*.wav",
         max_clip_duration_s=5,
-        remove_silence=True,
+        remove_silence=remove_silence,
         random_split_seed=10,
         split_count=0.1,
     )
@@ -263,9 +276,12 @@ def generate_feature_set(input_wav_dir: str, out_root_dir: str, label: str):
 # Wake word generated/TTS features (existing behavior)
 generate_feature_set(args.input_dir, args.output_dir, "generated")
 
-# Personal features (NEW)
+# Personal features
 generate_feature_set(args.personal_dir, args.personal_output_dir, "personal")
 
+# Reviewed false-positive / hard-negative features
+generate_feature_set(args.negative_dir, args.negative_output_dir, "reviewed negatives", remove_silence=False)
+
 END_TIME = datetime.now(timezone.utc).replace(microsecond=0)
 et = END_TIME - START_TIME
 print(f"\n{'=' * 80}")

cli/wake_word_sample_trainer

@@ -111,6 +111,16 @@ else
   echo "ℹ️  No personal features found at ${PERSONAL_FEATURES_DIR}/training (continuing without personal weighting)"
 fi
 
+# Reviewed false-positive features are optional hard negatives.
+REVIEWED_NEGATIVE_FEATURES_DIR="${WORK_DIR}/reviewed_negative_features"
+HAS_REVIEWED_NEGATIVE="false"
+if [ -d "${REVIEWED_NEGATIVE_FEATURES_DIR}/training" ] ; then
+  HAS_REVIEWED_NEGATIVE="true"
+  echo "✅ Found reviewed negative features: ${REVIEWED_NEGATIVE_FEATURES_DIR}/training (will weight as hard negatives)"
+else
+  echo "ℹ️  No reviewed negative features found at ${REVIEWED_NEGATIVE_FEATURES_DIR}/training (continuing with stock negatives)"
+fi
+
 cd "${WORK_DIR}"
 
 echo "===== Starting ${TRAINING_STEPS} training steps ====="
@@ -133,6 +143,7 @@ features:
   truth: true
   type: mmap
 __PERSONAL_FEATURE_MARKER__
+__REVIEWED_NEGATIVE_FEATURE_MARKER__
 - features_dir: __NEG_SPEECH__
   penalty_weight: 1.0
   sampling_weight: 12.0
@@ -208,6 +219,22 @@ else
   sed -i -e "/__PERSONAL_FEATURE_MARKER__/d" "${YAML_PATH}"
 fi
 
+# Insert/remove reviewed hard-negative block
+if [ "${HAS_REVIEWED_NEGATIVE}" = "true" ]; then
+  reviewed_negative_block="$(cat <<EOF
+- features_dir: ${REVIEWED_NEGATIVE_FEATURES_DIR}
+  penalty_weight: 1.25
+  sampling_weight: 8.0
+  truncation_strategy: random
+  truth: false
+  type: mmap
+EOF
+)"
+  perl -0777 -i -pe "s#__REVIEWED_NEGATIVE_FEATURE_MARKER__#${reviewed_negative_block}#g" "${YAML_PATH}"
+else
+  sed -i -e "/__REVIEWED_NEGATIVE_FEATURE_MARKER__/d" "${YAML_PATH}"
+fi
+
 echo "   Wrote training_parameters.yaml"
 rm -rf "${WORK_DIR}/trained_models/wakeword"
 
@@ -317,6 +344,7 @@ fi
 
 TRAINING_DONE="false"
 
+echo "🏋️ Starting model training and TFLite export (this is the longest stage)…"
 if run_attempt "Attempt 1/3: GPU training (default runtime profile)" ; then
   echo "✅ Training complete (GPU path)."
   TRAINING_DONE="true"
@@ -386,12 +414,24 @@ if [ "${TRAINING_DONE}" != "true" ]; then
 fi
 
 source_path="${WORK_DIR}/trained_models/wakeword/tflite_stream_state_internal_quant/stream_state_internal_quant.tflite"
+calibration_path="${WORK_DIR}/trained_models/wakeword/tflite_stream_state_internal_quant/detection_calibration.json"
 
 if [ ! -f "${source_path}" ] ; then
     echo "Output model not found! Training didn't complete successfully.  See ${TRAIN_LOG}"
     exit 1
 fi
 
+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 "${calibration_path}"; then
+    echo "✅ Detector calibration complete."
+else
+    echo "⚠️  Detector calibration failed; packaging with default detector settings."
+    rm -f "${calibration_path}" || :
+fi
+
 cp "${WORK_DIR}/trained_models/wakeword/model_summary.txt" "${OUTPUT_DIR}/logs/" || :
 cp -a "${WORK_DIR}/trained_models/wakeword/logs/train" "${OUTPUT_DIR}/logs/" || :
 cp -a "${WORK_DIR}/trained_models/wakeword/logs/validation" "${OUTPUT_DIR}/logs/" || :
@@ -404,24 +444,49 @@ tflite_path="${OUTPUT_DIR}/${tflite_filename}"
 cp "${source_path}" "${tflite_path}"
 
 json_path="${OUTPUT_DIR}/${wake_word_filename}.json"
-cat <<-EOF > "${json_path}"
-{
+export WAKE_WORD_TITLE LANGUAGE JSON_PATH="${json_path}" TFLITE_FILENAME="${tflite_filename}" CALIBRATION_PATH="${calibration_path}"
+echo "📦 Packaging final model artifacts…"
+"${PYTHON_BIN:-python}" - <<'PY'
+import json
+import os
+from pathlib import Path
+
+json_path = Path(os.environ["JSON_PATH"])
+calibration_path = Path(os.environ.get("CALIBRATION_PATH", ""))
+language = (os.environ.get("LANGUAGE", "en") or "en").strip().lower()
+probability_cutoff = 0.97
+sliding_window_size = 5
+
+if calibration_path.exists():
+    try:
+        calibration = json.loads(calibration_path.read_text(encoding="utf-8"))
+        probability_cutoff = float(calibration.get("probability_cutoff", probability_cutoff))
+        sliding_window_size = int(calibration.get("sliding_window_size", sliding_window_size))
+        print(
+            f"🎯 Using calibrated detector settings: "
+            f"cutoff={probability_cutoff:.2f}, window={sliding_window_size}"
+        )
+    except Exception as exc:
+        print(f"⚠️ Failed to read detector calibration ({exc}); using defaults.")
+
+meta = {
     "type": "micro",
-    "wake_word": "${WAKE_WORD_TITLE}",
+    "wake_word": os.environ["WAKE_WORD_TITLE"],
     "author": "Tater Totterson",
     "website": "https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git",
-    "model": "${tflite_filename}",
-    "trained_languages": ["en"],
+    "model": os.environ["TFLITE_FILENAME"],
+    "trained_languages": [language],
     "version": 2,
     "micro": {
-        "probability_cutoff": 0.97,
-        "sliding_window_size": 5,
+        "probability_cutoff": round(probability_cutoff, 2),
+        "sliding_window_size": sliding_window_size,
         "feature_step_size": 10,
         "tensor_arena_size": 30000,
-        "minimum_esphome_version": "2024.7.0"
-    }
+        "minimum_esphome_version": "2024.7.0",
+    },
 }
-EOF
+json_path.write_text(json.dumps(meta, indent=4) + "\n", encoding="utf-8")
+PY
 
 echo "Name:     ${WAKE_WORD_TITLE}"
 echo "Model:    ${tflite_path}"

dockerfile

@@ -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 ca-certificates nano less \
+    git wget curl unzip patch ca-certificates nano less \
  && rm -rf /var/lib/apt/lists/* \
  && mkdir -p /data
 
@@ -22,7 +22,7 @@ COPY --chown=root:root --chmod=0755 .bashrc /root/
 # Root-level entrypoints
 COPY --chown=root:root --chmod=0755 \
     train_wake_word \
-    run_recorder.sh \
+    run.sh \
     trainer_server.py \
     requirements.txt \
     /root/mww-scripts/
@@ -37,4 +37,4 @@ RUN chmod -R a+x /root/mww-scripts/cli
 COPY --chown=root:root --chmod=0644 static/index.html /root/mww-scripts/static/index.html
 
 # trainer server
-CMD ["/bin/bash", "-lc", "/root/mww-scripts/run_recorder.sh"]
+CMD ["/bin/bash", "-lc", "/root/mww-scripts/run.sh"]

run.sh

@@ -6,9 +6,9 @@ ROOTDIR="$(dirname "$(realpath "$0")")"
 # Training convention
 DATA_DIR="${DATA_DIR:-/data}"
 HOST="${REC_HOST:-0.0.0.0}"
-PORT="${REC_PORT:-8888}"
+PORT="${REC_PORT:-8789}"
 
-# Keep recorder deps separate from training venv
+# Keep trainer UI deps separate from the training venv
 VENV_DIR="${DATA_DIR}/.recorder-venv"
 PY="${VENV_DIR}/bin/python"
 PIP="${PY} -m pip"
@@ -17,6 +17,7 @@ PIN_FILE="${VENV_DIR}/.pinned_installed"
 FASTAPI_VERSION="${REC_FASTAPI_VERSION:-0.115.6}"
 UVICORN_VERSION="${REC_UVICORN_VERSION:-0.30.6}"
 PY_MULTIPART_VERSION="${REC_PY_MULTIPART_VERSION:-0.0.9}"
+ESPHOME_VERSION="${REC_ESPHOME_VERSION:-2026.4.0}"
 
 echo "microWakeWord Trainer UI (Docker)"
 echo "-> ROOTDIR:  ${ROOTDIR}"
@@ -42,10 +43,27 @@ if [[ ! -f "${PIN_FILE}" ]]; then
   ${PIP} install \
     "fastapi==${FASTAPI_VERSION}" \
     "uvicorn[standard]==${UVICORN_VERSION}" \
-    "python-multipart==${PY_MULTIPART_VERSION}"
+    "python-multipart==${PY_MULTIPART_VERSION}" \
+    "esphome==${ESPHOME_VERSION}"
   touch "${PIN_FILE}"
 else
   echo "Reusing existing trainer UI venv (no upgrades)"
+  if ! "${PY}" - "${ESPHOME_VERSION}" <<'PY' >/dev/null 2>&1
+import importlib.metadata
+import sys
+
+expected = sys.argv[1]
+installed = importlib.metadata.version("esphome")
+raise SystemExit(0 if installed == expected else 1)
+PY
+  then
+    echo "Firmware tab dependencies missing or stale; installing ESPHome firmware dependencies"
+    ${PIP} install \
+      "fastapi==${FASTAPI_VERSION}" \
+      "uvicorn[standard]==${UVICORN_VERSION}" \
+      "python-multipart==${PY_MULTIPART_VERSION}" \
+      "esphome==${ESPHOME_VERSION}"
+  fi
 fi
 
 # -----------------------------
@@ -54,6 +72,9 @@ fi
 export DATA_DIR="${DATA_DIR}"
 export STATIC_DIR="${ROOTDIR}/static"
 export PERSONAL_DIR="${DATA_DIR}/personal_samples"
+export CAPTURED_DIR="${DATA_DIR}/captured_audio"
+export NEGATIVE_DIR="${DATA_DIR}/negative_samples"
+export TRAINED_WAKE_WORDS_DIR="${DATA_DIR}/trained_wake_words"
 
 # IMPORTANT: leave training venv creation to /api/train inside trainer_server.py
 # but still set TRAIN_CMD so the server knows how to invoke training once ready

train_wake_word

@@ -150,6 +150,7 @@ if ${CLEANUP_WORK_DIR} ; then
       "${DATA_DIR}/work/wake_word_samples" \
       "${DATA_DIR}/work/wake_word_samples_augmented" \
       "${DATA_DIR}/work/personal_augmented_features" \
+      "${DATA_DIR}/work/reviewed_negative_features" \
       "${DATA_DIR}/work/last_wake_word" || :
 fi