Add Microphone Mute Switch Entity
Implements the core mute functionality. Adds a new MicMuteHandler class that subscribes to the set_mic_mute event. This handler updates the state.mic_muted flag and publishes secondary mic_muted or mic_unmuted events for LED feedback. Modifies the process_audio function to check the state.mic_muted flag in its main loop, pausing audio processing when the flag is True.
This commit is contained in:
@@ -12,7 +12,7 @@ from typing import Dict, List, Optional, Union
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import numpy as np
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import sounddevice as sd
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from .event_bus import EventBus
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from .event_bus import EventBus, EventHandler, subscribe # ADDED EventHandler, subscribe
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from .led_controller import LedController
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from .microwakeword import MicroWakeWord, MicroWakeWordFeatures
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from .models import AvailableWakeWord, Preferences, ServerState, WakeWordType
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@@ -34,12 +34,29 @@ if is_arm():
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else:
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_LIB_DIR = _REPO_DIR / "lib" / "linux_amd64"
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# -----------------------------------------------------------------------------
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# --- ADDED MIC MUTE HANDLER ---
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# -----------------------------------------------------------------------------
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class MicMuteHandler(EventHandler):
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"""Event handler for mic mute switch."""
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@subscribe
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def set_mic_mute(self, data: dict):
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is_muted = data.get("state", False)
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if self.state.mic_muted != is_muted:
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self.state.mic_muted = is_muted
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_LOGGER.debug("Mic muted = %s", is_muted)
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if is_muted:
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self.state.event_bus.publish("mic_muted")
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else:
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self.state.event_bus.publish("mic_unmuted")
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# -----------------------------------------------------------------------------
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async def main() -> None:
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parser = argparse.ArgumentParser()
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# ... (all parser arguments are the same) ...
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parser.add_argument("--name", required=True)
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parser.add_argument(
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"--audio-input-device",
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@@ -64,7 +81,6 @@ async def main() -> None:
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type=float,
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help="Seconds before wake word can be activated again",
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)
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#
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parser.add_argument(
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"--oww-melspectrogram-model",
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default=_OWW_DIR / "melspectrogram.tflite",
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@@ -75,16 +91,13 @@ async def main() -> None:
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default=_OWW_DIR / "embedding_model.tflite",
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help="Path to openWakeWord embedding model",
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)
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#
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parser.add_argument(
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"--wakeup-sound", default=str(_SOUNDS_DIR / "wake_word_triggered.flac")
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)
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parser.add_argument(
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"--timer-finished-sound", default=str(_SOUNDS_DIR / "timer_finished.flac")
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)
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#
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parser.add_argument("--preferences-file", default=_REPO_DIR / "preferences.json")
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#
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parser.add_argument(
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"--host",
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default="0.0.0.0",
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@@ -93,7 +106,6 @@ async def main() -> None:
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parser.add_argument(
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"--port", type=int, default=6053, help="Port for ESPHome server (default: 6053)"
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)
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#
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parser.add_argument(
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"--led-interface",
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choices=["spi", "gpio"],
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@@ -112,7 +124,6 @@ async def main() -> None:
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default=12,
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help="GPIO pin for LED data (for Grove)",
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)
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#
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parser.add_argument(
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"--debug", action="store_true", help="Print DEBUG messages to console"
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)
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@@ -121,23 +132,16 @@ async def main() -> None:
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logging.basicConfig(level=logging.DEBUG if args.debug else logging.INFO)
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_LOGGER.debug(args)
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# Get the asyncio event loop
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loop = asyncio.get_running_loop()
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# Create the event bus
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event_bus = EventBus()
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# Load available wake words
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wake_word_dirs = [Path(ww_dir) for ww_dir in args.wake_word_dir]
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available_wake_words: Dict[str, AvailableWakeWord] = {}
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for wake_word_dir in wake_word_dirs:
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for model_config_path in wake_word_dir.glob("*.json"):
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model_id = model_config_path.stem
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if model_id == args.stop_model:
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# Don't show stop model as an available wake word
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continue
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with open(model_config_path, "r", encoding="utf-8") as model_config_file:
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model_config = json.load(model_config_file)
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model_type = model_config["type"]
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@@ -151,7 +155,6 @@ async def main() -> None:
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_LOGGER.debug("Available wake words: %s", list(sorted(available_wake_words.keys())))
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# Load preferences
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preferences_path = Path(args.preferences_file)
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if preferences_path.exists():
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_LOGGER.debug("Loading preferences: %s", preferences_path)
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@@ -164,34 +167,26 @@ async def main() -> None:
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libtensorflowlite_c_path = _LIB_DIR / "libtensorflowlite_c.so"
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_LOGGER.debug("libtensorflowlite_c path: %s", libtensorflowlite_c_path)
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# Load wake/stop models
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wake_models: Dict[str, Union[MicroWakeWord, OpenWakeWord]] = {}
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if preferences.active_wake_words:
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# Load preferred models
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for wake_word_id in preferences.active_wake_words:
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wake_word = available_wake_words.get(wake_word_id)
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if wake_word is None:
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_LOGGER.warning("Unrecognized wake word id: %s", wake_word_id)
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continue
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_LOGGER.debug("Loading wake model: %s", wake_word_id)
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wake_models[wake_word_id] = wake_word.load(libtensorflowlite_c_path)
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if not wake_models:
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# Load default model
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wake_word_id = args.wake_model
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wake_word = available_wake_words[wake_word_id]
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_LOGGER.debug("Loading wake model: %s", wake_word_id)
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wake_models[wake_word_id] = wake_word.load(libtensorflowlite_c_path)
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# TODO: allow openWakeWord for "stop"
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stop_model: Optional[MicroWakeWord] = None
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for wake_word_dir in wake_word_dirs:
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stop_config_path = wake_word_dir / f"{args.stop_model}.json"
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if not stop_config_path.exists():
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continue
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_LOGGER.debug("Loading stop model: %s", stop_config_path)
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stop_model = MicroWakeWord.from_config(
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stop_config_path, libtensorflowlite_c_path
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@@ -200,7 +195,6 @@ async def main() -> None:
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assert stop_model is not None
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# Create media players with saved volume
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music_player = MpvMediaPlayer(
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device=args.audio_output_device, initial_volume=preferences.volume_level
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)
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@@ -230,13 +224,14 @@ async def main() -> None:
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refractory_seconds=args.refractory_seconds,
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)
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# Initialize the LED controller AFTER state is created
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led_controller = LedController(
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state,
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interface=args.led_interface,
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clock_pin=args.led_clock_pin,
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data_pin=args.led_data_pin,
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)
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# --- ADDED: Instantiate Mic Mute Handler ---
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mic_mute_handler = MicMuteHandler(state)
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process_audio_thread = threading.Thread(
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target=process_audio, args=(state,), daemon=True
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@@ -250,7 +245,6 @@ async def main() -> None:
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lambda: VoiceSatelliteProtocol(state), host=args.host, port=args.port
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)
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# Auto discovery (zeroconf, mDNS)
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discovery = HomeAssistantZeroconf(port=args.port, name=args.name)
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await discovery.register_server()
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@@ -281,60 +275,55 @@ async def main() -> None:
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def process_audio(state: ServerState):
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"""Process audio chunks from the microphone."""
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# ... (variable initializations are the same) ...
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wake_words: List[Union[MicroWakeWord, OpenWakeWord]] = []
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micro_features: Optional[MicroWakeWordFeatures] = None
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micro_inputs: List[np.ndarray] = []
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oww_features: Optional[OpenWakeWordFeatures] = None
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oww_inputs: List[np.ndarray] = []
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has_oww = False
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last_active: Optional[float] = None
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try:
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while True:
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audio_chunk = state.audio_queue.get()
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if audio_chunk is None:
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break
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if state.satellite is None:
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# --- THIS IS THE CHANGE ---
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# Check for mute status
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if state.mic_muted:
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time.sleep(0.1)
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continue
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audio_chunk = state.audio_queue.get()
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# ... (rest of the function is the same) ...
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if audio_chunk is None:
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break
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if state.satellite is None:
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continue
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if (not wake_words) or (state.wake_words_changed and state.wake_words):
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# Update list of wake word models to process
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state.wake_words_changed = False
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wake_words = [ww for ww in state.wake_words.values() if ww.is_active]
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has_oww = False
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for wake_word in wake_words:
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if isinstance(wake_word, OpenWakeWord):
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has_oww = True
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if micro_features is None:
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micro_features = MicroWakeWordFeatures(
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libtensorflowlite_c_path=state.libtensorflowlite_c_path,
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)
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if has_oww and (oww_features is None):
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oww_features = OpenWakeWordFeatures(
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melspectrogram_model=state.oww_melspectrogram_path,
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embedding_model=state.oww_embedding_path,
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libtensorflowlite_c_path=state.libtensorflowlite_c_path,
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)
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try:
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state.satellite.handle_audio(audio_chunk)
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assert micro_features is not None
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micro_inputs.clear()
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micro_inputs.extend(micro_features.process_streaming(audio_chunk))
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if has_oww:
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assert oww_features is not None
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oww_inputs.clear()
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oww_inputs.extend(oww_features.process_streaming(audio_chunk))
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for wake_word in wake_words:
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activated = False
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if isinstance(wake_word, MicroWakeWord):
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@@ -346,27 +335,21 @@ def process_audio(state: ServerState):
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for prob in wake_word.process_streaming(oww_input):
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if prob > 0.5:
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activated = True
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if activated:
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# Check refractory
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now = time.monotonic()
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if (last_active is None) or (
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(now - last_active) > state.refractory_seconds
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):
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state.satellite.wakeup(wake_word)
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last_active = now
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# Always process to keep state correct
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stopped = False
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for micro_input in micro_inputs:
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if state.stop_word.process_streaming(micro_input):
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stopped = True
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if stopped and state.stop_word.is_active:
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state.satellite.stop()
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except Exception:
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_LOGGER.exception("Unexpected error handling audio")
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except Exception:
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_LOGGER.exception("Unexpected error processing audio")
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