jetson/ros2_ws/src/saltybot_face_bridge/config/face_bridge_params.yaml

@@ -1,13 +0,0 @@
-face_bridge:
-  # HTTP server endpoint for face display
-  face_server_url: "http://localhost:3000/face/{id}"  # {id} replaced with expression ID
-
-  # HTTP request settings
-  http_timeout: 2.0                                  # Request timeout in seconds
-  update_interval: 0.1                               # Update check interval in seconds
-
-  # State to expression mapping:
-  #   0 = Tracking (IDLE, THROTTLED)
-  #   1 = Alert (LISTENING, wake word)
-  #   3 = Searching (THINKING)
-  #   4 = Social (SPEAKING)

jetson/ros2_ws/src/saltybot_face_bridge/launch/face_bridge.launch.py

@@ -1,40 +0,0 @@
-"""Launch file for face display bridge node."""
-
-from launch import LaunchDescription
-from launch_ros.actions import Node
-from launch.substitutions import LaunchConfiguration
-from launch.actions import DeclareLaunchArgument
-
-
-def generate_launch_description():
-    """Generate launch description."""
-    # Declare arguments
-    url_arg = DeclareLaunchArgument(
-        "face_server_url",
-        default_value="http://localhost:3000/face/{id}",
-        description="Face display server HTTP endpoint"
-    )
-    timeout_arg = DeclareLaunchArgument(
-        "http_timeout",
-        default_value="2.0",
-        description="HTTP request timeout in seconds"
-    )
-
-    # Create node
-    face_bridge_node = Node(
-        package="saltybot_face_bridge",
-        executable="face_bridge_node",
-        name="face_bridge",
-        parameters=[
-            {"face_server_url": LaunchConfiguration("face_server_url")},
-            {"http_timeout": LaunchConfiguration("http_timeout")},
-            {"update_interval": 0.1},
-        ],
-        output="screen",
-    )
-
-    return LaunchDescription([
-        url_arg,
-        timeout_arg,
-        face_bridge_node,
-    ])

jetson/ros2_ws/src/saltybot_face_bridge/package.xml

@@ -1,26 +0,0 @@
-<?xml version="1.0"?>
-<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
-<package format="3">
-  <name>saltybot_face_bridge</name>
-  <version>0.1.0</version>
-  <description>
-    Face display bridge node for orchestrator state to face expression mapping.
-    Maps social/orchestrator state to face display WebSocket API.
-  </description>
-  <maintainer email="sl-controls@saltylab.local">sl-controls</maintainer>
-  <license>MIT</license>
-
-  <depend>rclpy</depend>
-  <depend>std_msgs</depend>
-
-  <buildtool_depend>ament_python</buildtool_depend>
-
-  <test_depend>ament_copyright</test_depend>
-  <test_depend>ament_flake8</test_depend>
-  <test_depend>ament_pep257</test_depend>
-  <test_depend>python3-pytest</test_depend>
-
-  <export>
-    <build_type>ament_python</build_type>
-  </export>
-</package>

jetson/ros2_ws/src/saltybot_face_bridge/setup.cfg

@@ -1,4 +0,0 @@
-[develop]
-script-dir=$base/lib/saltybot_face_bridge
-[egg_info]
-tag_date = 0

jetson/ros2_ws/src/saltybot_face_bridge/setup.py

@@ -1,27 +0,0 @@
-from setuptools import setup
-
-package_name = "saltybot_face_bridge"
-
-setup(
-    name=package_name,
-    version="0.1.0",
-    packages=[package_name],
-    data_files=[
-        ("share/ament_index/resource_index/packages", [f"resource/{package_name}"]),
-        (f"share/{package_name}", ["package.xml"]),
-        (f"share/{package_name}/launch", ["launch/face_bridge.launch.py"]),
-        (f"share/{package_name}/config", ["config/face_bridge_params.yaml"]),
-    ],
-    install_requires=["setuptools"],
-    zip_safe=True,
-    maintainer="sl-controls",
-    maintainer_email="sl-controls@saltylab.local",
-    description="Face display bridge for orchestrator state mapping",
-    license="MIT",
-    tests_require=["pytest"],
-    entry_points={
-        "console_scripts": [
-            "face_bridge_node = saltybot_face_bridge.face_bridge_node:main",
-        ],
-    },
-)

jetson/ros2_ws/src/saltybot_social/config/wake_word_params.yaml

@@ -13,7 +13,7 @@ wake_word_node:
 
     # Path to .npy template file (log-mel features of 'hey salty' recording).
     # Leave empty for passive mode (no detections fired).
-    template_path: "jetson/ros2_ws/src/saltybot_social/models/hey_salty.npy"   # Issue #393
+    template_path: ""           # e.g. "/opt/saltybot/models/hey_salty.npy"
 
     n_fft:  512     # FFT size for mel spectrogram
     n_mels: 40      # mel filterbank bands

jetson/ros2_ws/src/saltybot_social/models/README.md

@@ -1,118 +0,0 @@
-# SaltyBot Wake Word Models
-
-## Current Model: hey_salty.npy
-
-**Issue #393** — Custom OpenWakeWord model for "hey salty" wake phrase detection.
-
-### Model Details
-
-- **File**: `hey_salty.npy`
-- **Type**: Log-mel spectrogram template (numpy array)
-- **Shape**: `(40, 61)` — 40 mel bands, ~61 time frames
-- **Generation Method**: Synthetic speech using sine-wave approximation
-- **Integration**: Used by `wake_word_node.py` via cosine similarity matching
-
-### How It Works
-
-The `wake_word_node` subscribes to raw PCM-16 audio at 16 kHz mono and:
-
-1. Maintains a sliding window of the last 1.5 seconds of audio
-2. Extracts log-mel spectrogram features every 100 ms
-3. Compares the log-mel features to this template via cosine similarity
-4. Fires a detection event (`/saltybot/wake_word_detected → True`) when:
-   - **Energy gate**: RMS amplitude > threshold (default 0.02)
-   - **Match gate**: Cosine similarity > threshold (default 0.82)
-5. Applies cooldown (default 2.0 s) to prevent rapid re-fires
-
-### Configuration (wake_word_params.yaml)
-
-```yaml
-template_path: "jetson/ros2_ws/src/saltybot_social/models/hey_salty.npy"
-energy_threshold: 0.02      # RMS gate
-match_threshold: 0.82       # cosine-similarity threshold
-cooldown_s: 2.0             # minimum gap between detections (s)
-```
-
-Adjust `match_threshold` to control sensitivity:
-- **Lower** (e.g., 0.75) → more sensitive, higher false-positive rate
-- **Higher** (e.g., 0.90) → less sensitive, more robust to noise
-
-## Retraining with Real Recordings (Future)
-
-To improve accuracy, follow these steps on a development machine:
-
-### 1. Collect Training Data
-
-Record 10–20 natural utterances of "hey salty" in varied conditions:
-- Different speakers (male, female, child)
-- Different background noise (quiet room, kitchen, outdoor)
-- Different distances from microphone
-
-```bash
-# Using arecord (ALSA) on Jetson or Linux:
-for i in {1..20}; do
-  echo "Recording sample $i. Say 'hey salty'..."
-  arecord -r 16000 -f S16_LE -c 1 "hey_salty_${i}.wav"
-done
-```
-
-### 2. Extract Templates from Training Data
-
-Use the same DSP pipeline as `wake_word_node.py`:
-
-```python
-import numpy as np
-from wake_word_node import compute_log_mel
-
-samples = []
-for wav_file in glob("hey_salty_*.wav"):
-    sr, data = scipy.io.wavfile.read(wav_file)
-    # Resample to 16kHz if needed
-    float_data = data / 32768.0  # convert PCM-16 to [-1, 1]
-    log_mel = compute_log_mel(float_data, sr=16000, n_fft=512, n_mels=40)
-    samples.append(log_mel)
-
-# Pad to same length, average
-max_len = max(m.shape[1] for m in samples)
-padded = [np.pad(m, ((0, 0), (0, max_len - m.shape[1])), mode='edge')
-          for m in samples]
-template = np.mean(padded, axis=0).astype(np.float32)
-np.save("hey_salty.npy", template)
-```
-
-### 3. Test and Tune
-
-1. Replace the current template with your new one
-2. Test with `wake_word_node` in real environment
-3. Adjust `match_threshold` in `wake_word_params.yaml` to find the sweet spot
-4. Collect false-positive and false-negative cases; add them to training set
-5. Retrain
-
-### 4. Version Control
-
-Once satisfied, replace `models/hey_salty.npy` and commit:
-
-```bash
-git add jetson/ros2_ws/src/saltybot_social/models/hey_salty.npy
-git commit -m "refactor: hey salty template with real training data (v2)"
-```
-
-## Files
-
-- `generate_wake_word_template.py` — Script to synthesize and generate template
-- `hey_salty.npy` — Current template (generated from synthetic speech)
-- `README.md` — This file
-
-## References
-
-- `wake_word_node.py` — Wake word detection node (cosine similarity, energy gating)
-- `wake_word_params.yaml` — Detection parameters
-- `test_wake_word.py` — Unit tests for DSP pipeline
-
-## Future Improvements
-
-- [ ] Collect real user recordings
-- [ ] Fine-tune with multiple speakers/environments
-- [ ] Evaluate false-positive rate
-- [ ] Consider speaker-adaptive templates (per user)
-- [ ] Explore end-to-end learned models (TinyWakeWord, etc.)