imonlinux
943a8a0d5c
Fix MpvMediaPlayer Race Condition Goal: The mpv library runs in its own thread. When a track finishes, its callback (_on_idle_active) runs in that thread and tries to modify the satellite's state, creating a race condition. We will fix this by "marshaling" that callback onto the main asyncio loop. added loop=loop to the MpvMediaPlayer constructors
336 lines
13 KiB
Python
336 lines
13 KiB
Python
#!/usr/bin/env python3
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import argparse
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import asyncio
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import json
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import logging
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import threading
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import time
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from pathlib import Path
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from queue import Queue
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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, EventHandler, subscribe
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from .led_controller import LedController
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from .mqtt_controller import MqttController
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from .microwakeword import MicroWakeWord, MicroWakeWordFeatures
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from .models import AvailableWakeWord, Preferences, ServerState, WakeWordType
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from .mpv_player import MpvMediaPlayer
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from .openwakeword import OpenWakeWord, OpenWakeWordFeatures
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from .satellite import VoiceSatelliteProtocol
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from .util import get_mac, is_arm
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from .zeroconf import HomeAssistantZeroconf
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_LOGGER = logging.getLogger(__name__)
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_MODULE_DIR = Path(__file__).parent
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_REPO_DIR = _MODULE_DIR.parent
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_WAKEWORDS_DIR = _REPO_DIR / "wakewords"
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_OWW_DIR = _WAKEWORDS_DIR / "openWakeWord"
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_SOUNDS_DIR = _REPO_DIR / "sounds"
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if is_arm():
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_LIB_DIR = _REPO_DIR / "lib" / "linux_arm64"
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else:
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_LIB_DIR = _REPO_DIR / "lib" / "linux_amd64"
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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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def __init__(self, state, mqtt_controller: Optional[MqttController]):
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super().__init__(state)
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self.mqtt_controller = mqtt_controller
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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 self.mqtt_controller:
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self.mqtt_controller.publish_mute_state(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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_LOGGER.debug("Clearing stale audio queue...")
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while not self.state.audio_queue.empty():
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try:
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self.state.audio_queue.get_nowait()
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except Queue.Empty:
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break
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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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parser.add_argument("--name", required=True)
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parser.add_argument(
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"--audio-input-device",
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default="default",
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help="sounddevice name for input device",
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)
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parser.add_argument("--audio-input-block-size", type=int, default=1024)
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parser.add_argument("--audio-output-device", help="mpv name for output device")
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parser.add_argument(
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"--wake-word-dir",
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default=[_WAKEWORDS_DIR],
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action="append",
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help="Directory with wake word models (.tflite) and configs (.json)",
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)
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parser.add_argument(
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"--wake-model", default="okay_nabu", help="Id of active wake model"
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)
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parser.add_argument("--stop-model", default="stop", help="Id of stop model")
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parser.add_argument(
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"--refractory-seconds",
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default=2.0,
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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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parser.add_argument(
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"--oww-melspectrogram-model",
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default=_OWW_DIR / "melspectrogram.tflite",
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help="Path to openWakeWord melspectrogram model",
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)
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parser.add_argument(
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"--oww-embedding-model",
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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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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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parser.add_argument("--preferences-file", default=_REPO_DIR / "preferences.json")
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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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help="Address for ESPHome server (default: 0.0.0.0)",
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)
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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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parser.add_argument(
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"--led-type",
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choices=["dotstar", "neopixel"],
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default="dotstar",
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help="Type of LED strip (default: dotstar for APA102)",
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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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default="spi",
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help="Interface for LEDs (default: spi)",
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)
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parser.add_argument(
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"--led-clock-pin",
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type=int,
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default=13,
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help="GPIO pin for LED clock (for Grove)",
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)
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parser.add_argument(
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"--led-data-pin",
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type=int,
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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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parser.add_argument(
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"--num-leds",
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type=int,
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default=3,
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help="Number of LEDs in the strip",
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)
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parser.add_argument("--mqtt-host", help="MQTT broker host")
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parser.add_argument("--mqtt-port", type=int, default=1883, help="MQTT broker port")
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parser.add_argument("--mqtt-username", help="MQTT broker username")
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parser.add_argument("--mqtt-password", help="MQTT broker password")
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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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args = parser.parse_args()
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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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loop = asyncio.get_running_loop()
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event_bus = EventBus()
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preferences_path = Path(args.preferences_file)
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if preferences_path.exists():
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with open(preferences_path, "r", encoding="utf-8") as preferences_file:
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preferences_dict = json.load(preferences_file)
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preferences = Preferences(**preferences_dict)
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else:
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preferences = Preferences()
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preferences.num_leds = getattr(preferences, 'num_leds', args.num_leds)
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available_wake_words: Dict[str, AvailableWakeWord] = {}
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for wake_word_dir in [Path(d) for d in args.wake_word_dir]:
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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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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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available_wake_words[model_id] = AvailableWakeWord(
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id=model_id,
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type=WakeWordType(model_type),
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wake_word=model_config["wake_word"],
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trained_languages=model_config.get("trained_languages", []),
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config_path=model_config_path,
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)
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_LOGGER.debug("Available wake words: %s", list(sorted(available_wake_words.keys())))
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libtensorflowlite_c_path = _LIB_DIR / "libtensorflowlite_c.so"
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wake_models: Dict[str, Union[MicroWakeWord, OpenWakeWord]] = {}
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if preferences.active_wake_words:
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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: continue
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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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wake_word_id = args.wake_model
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wake_word = available_wake_words.get(wake_word_id)
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if wake_word:
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wake_models[wake_word_id] = wake_word.load(libtensorflowlite_c_path)
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stop_model: Optional[MicroWakeWord] = None
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for wake_word_dir in [Path(d) for d in args.wake_word_dir]:
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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(): continue
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stop_model = MicroWakeWord.from_config(stop_config_path, libtensorflowlite_c_path)
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break
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assert stop_model is not None
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music_player = MpvMediaPlayer(
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loop=loop, # <-- MODIFIED
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device=args.audio_output_device,
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initial_volume=preferences.volume_level
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)
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tts_player = MpvMediaPlayer(
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loop=loop, # <-- MODIFIED
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device=args.audio_output_device,
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initial_volume=preferences.volume_level
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)
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state = ServerState(
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name=args.name,
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mac_address=get_mac(),
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audio_queue=Queue(),
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entities=[],
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available_wake_words=available_wake_words,
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wake_words=wake_models,
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stop_word=stop_model,
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music_player=music_player,
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tts_player=tts_player,
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wakeup_sound=args.wakeup_sound,
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timer_finished_sound=args.timer_finished_sound,
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preferences=preferences,
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preferences_path=preferences_path,
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libtensorflowlite_c_path=libtensorflowlite_c_path,
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event_bus=event_bus,
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loop=loop,
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oww_melspectrogram_path=Path(args.oww_melspectrogram_model),
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oww_embedding_path=Path(args.oww_embedding_model),
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refractory_seconds=args.refractory_seconds,
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)
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led_controller = LedController(
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state,
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led_type=args.led_type,
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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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num_leds=state.preferences.num_leds,
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)
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mqtt_controller: Optional[MqttController] = None
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if args.mqtt_host:
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mqtt_controller = MqttController(state=state, host=args.mqtt_host, port=args.mqtt_port, username=args.mqtt_username, password=args.mqtt_password)
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mqtt_controller.start()
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mic_mute_handler = MicMuteHandler(state, mqtt_controller)
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process_audio_thread = threading.Thread(target=process_audio, args=(state,), daemon=True)
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process_audio_thread.start()
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def sd_callback(indata, _frames, _time, _status): state.audio_queue.put_nowait(bytes(indata))
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server = await loop.create_server(lambda: VoiceSatelliteProtocol(state), host=args.host, port=args.port)
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discovery = HomeAssistantZeroconf(port=args.port, name=args.name)
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await discovery.register_server()
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try:
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with sd.RawInputStream(samplerate=16000, blocksize=args.audio_input_block_size, device=args.audio_input_device, dtype="int16", channels=1, callback=sd_callback):
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async with server:
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_LOGGER.info("Server started (host=%s, port=%s)", args.host, args.port)
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await server.serve_forever()
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except KeyboardInterrupt: pass
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finally:
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if mqtt_controller: mqtt_controller.stop()
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state.audio_queue.put_nowait(None)
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process_audio_thread.join()
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_LOGGER.debug("Server stopped")
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def process_audio(state: ServerState):
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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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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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if audio_chunk is None: break
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if state.satellite is None: continue
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if (not wake_words) or (state.wake_words_changed and state.wake_words):
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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 = any(isinstance(ww, OpenWakeWord) for ww in wake_words)
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if micro_features is None:
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micro_features = MicroWakeWordFeatures(libtensorflowlite_c_path=state.libtensorflowlite_c_path)
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if has_oww and (oww_features is None):
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oww_features = OpenWakeWordFeatures(melspectrogram_model=state.oww_melspectrogram_path, embedding_model=state.oww_embedding_path, libtensorflowlite_c_path=state.libtensorflowlite_c_path)
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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(); 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(); 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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if any(wake_word.process_streaming(mi) for mi in micro_inputs): activated = True
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elif isinstance(wake_word, OpenWakeWord):
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if any(p > 0.5 for oi in oww_inputs for p in wake_word.process_streaming(oi)): activated = True
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if activated:
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now = time.monotonic()
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if (last_active is None) or ((now - last_active) > state.refractory_seconds):
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state.satellite.wakeup(wake_word)
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last_active = now
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if any(state.stop_word.process_streaming(mi) for mi in micro_inputs) and state.stop_word.is_active:
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state.satellite.stop()
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except Exception: _LOGGER.exception("Unexpected error handling audio")
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except Exception: _LOGGER.exception("Unexpected error processing audio")
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if __name__ == "__main__":
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print("--- __main__ block executing ---")
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asyncio.run(main())
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