Fresh start - excluded large ROM JSON files

home-assistant/openwakeword-mqtt.py

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+#!/usr/bin/env python3
+"""
+openWakeWord to MQTT publisher
+Publishes wake word detections to Home Assistant MQTT broker
+"""
+
+import time
+import json
+import paho.mqtt.client as mqtt
+import openwakeword
+from openwakeword.model import Model
+import numpy as np
+import sounddevice as sd
+
+# MQTT Configuration
+MQTT_HOST = "192.168.0.39"  # Home Assistant IP
+MQTT_PORT = 1883
+MQTT_USER = "your_mqtt_username"  # Replace with your MQTT username
+MQTT_PASS = "your_mqtt_password"  # Replace with your MQTT password
+MQTT_TOPIC = "homeassistant/openwakeword/detection"
+
+# Wake word detection threshold (0.0 - 1.0)
+THRESHOLD = 0.5
+
+# Audio settings
+CHUNK_SIZE = 1600  # 100ms of audio @ 16kHz
+
+def on_connect(client, userdata, flags, rc):
+    if rc == 0:
+        print(f"Connected to MQTT broker at {MQTT_HOST}")
+    else:
+        print(f"Failed to connect, return code {rc}")
+
+def publish_detection(wake_word, confidence):
+    payload = json.dumps({
+        "wake_word": wake_word,
+        "confidence": float(confidence),
+        "timestamp": time.time()
+    })
+    client.publish(MQTT_TOPIC, payload)
+    print(f"Published: {wake_word} ({confidence:.2f})")
+
+def main():
+    global client
+
+    # Initialize MQTT client
+    client = mqtt.Client(client_id="openwakeword")
+    client.username_pw_set(MQTT_USER, MQTT_PASS)
+    client.on_connect = on_connect
+    client.connect(MQTT_HOST, MQTT_PORT, 60)
+    client.loop_start()
+
+    # Initialize openwakeword model
+    print("Loading openwakeword models...")
+    oww_model = Model()
+
+    # Get audio stream (adjust for your microphone)
+    # Using sounddevice for microphone input
+    import sounddevice as sd
+
+    def audio_callback(indata, frames, time, status):
+        if status:
+            print(status)
+        # Process audio for wake word detection
+        audio_data = indata.flatten()
+        predictions = oww_model.predict(audio_data)
+
+        # Check for detections
+        for wake_word, score in predictions.items():
+            if score >= THRESHOLD:
+                publish_detection(wake_word, score)
+
+    print("Listening for wake words...")
+    print("Press Ctrl+C to stop")
+
+    # Start streaming from microphone
+    with sd.InputStream(
+        channels=1,
+        samplerate=16000,
+        dtype='int16',
+        blocksize=CHUNK_SIZE,
+        callback=audio_callback
+    ):
+        try:
+            while True:
+                time.sleep(0.1)
+        except KeyboardInterrupt:
+            print("\nStopping...")
+            client.loop_stop()
+            client.disconnect()
+
+if __name__ == "__main__":
+    main()