334 lines
12 KiB
Python
334 lines
12 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
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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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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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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-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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"--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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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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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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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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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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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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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:
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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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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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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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)
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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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device=args.audio_output_device, initial_volume=preferences.volume_level
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)
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tts_player = MpvMediaPlayer(
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device=args.audio_output_device, 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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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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process_audio_thread = threading.Thread(
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target=process_audio, args=(state,), daemon=True
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)
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process_audio_thread.start()
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def sd_callback(indata, _frames, _time, _status):
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state.audio_queue.put_nowait(bytes(indata))
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server = await loop.create_server(
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lambda: VoiceSatelliteProtocol(state), host=args.host, port=args.port
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)
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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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_LOGGER.debug("Opening audio input device: %s", args.audio_input_device)
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with sd.RawInputStream(
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samplerate=16000,
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blocksize=args.audio_input_block_size,
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device=args.audio_input_device,
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dtype="int16",
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channels=1,
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callback=sd_callback,
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):
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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:
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pass
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finally:
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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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# -----------------------------------------------------------------------------
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def process_audio(state: ServerState):
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"""Process audio chunks from the microphone."""
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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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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 = 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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for micro_input in micro_inputs:
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if wake_word.process_streaming(micro_input):
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activated = True
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elif isinstance(wake_word, OpenWakeWord):
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for oww_input in oww_inputs:
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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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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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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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# -----------------------------------------------------------------------------
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if __name__ == "__main__":
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asyncio.run(main())
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