imonlinux
0f1ceefc5c
Goal: satellite.py currently uses a set of boolean flags (_is_streaming_audio, _tts_played, _run_finished, _is_speaking) to figure out what it's doing. This is hard to read and easy to break. Replaced this with a clean, formal state machine (using an Enum). This makes the logic explicit and much easier to manage. added the SatelliteState Enum at the top and removed the media_player_entity from ServerState as it's better managed by the satellite itself
111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
"""Shared models."""
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import asyncio
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import json
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import logging
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from dataclasses import asdict, dataclass, field
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from enum import Enum
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from pathlib import Path
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from queue import Queue
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from typing import TYPE_CHECKING, Dict, List, Optional, Union
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from .event_bus import EventBus
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if TYPE_CHECKING:
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from .entity import ESPHomeEntity, MediaPlayerEntity
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from .microwakeword import MicroWakeWord
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from .mpv_player import MpvMediaPlayer
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from .openwakeword import OpenWakeWord
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from .satellite import VoiceSatelliteProtocol
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_LOGGER = logging.getLogger(__name__)
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class SatelliteState(str, Enum):
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"""Voice satellite state."""
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IDLE = "idle"
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LISTENING = "listening" # Wake word detected
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THINKING = "thinking" # VAD, waiting for STT/intent
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RESPONDING = "responding" # Playing TTS
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ERROR = "error"
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class WakeWordType(str, Enum):
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MICRO_WAKE_WORD = "micro"
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OPEN_WAKE_WORD = "openWakeWord"
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@dataclass
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class AvailableWakeWord:
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id: str
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type: WakeWordType
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wake_word: str
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trained_languages: List[str]
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config_path: Path
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def load(
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self, libtensorflowlite_c_path: Path
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) -> "Union[MicroWakeWord, OpenWakeWord]":
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if self.type == WakeWordType.MICRO_WAKE_WORD:
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from .microwakeword import MicroWakeWord
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return MicroWakeWord.from_config(
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config_path=self.config_path,
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libtensorflowlite_c_path=libtensorflowlite_c_path,
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)
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if self.type == WakeWordType.OPEN_WAKE_WORD:
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from .openwakeword import OpenWakeWord
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return OpenWakeWord.from_config(
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config_path=self.config_path,
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libtensorflowlite_c_path=libtensorflowlite_c_path,
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)
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raise ValueError(f"Unexpected wake word type: {self.type}")
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@dataclass
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class Preferences:
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active_wake_words: List[str] = field(default_factory=list)
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volume_level: float = 1.0
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num_leds: int = 3
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@dataclass
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class ServerState:
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# --- Fields WITHOUT default values ---
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name: str
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mac_address: str
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audio_queue: "Queue[Optional[bytes]]"
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entities: "List[ESPHomeEntity]"
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available_wake_words: "Dict[str, AvailableWakeWord]"
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wake_words: "Dict[str, Union[MicroWakeWord, OpenWakeWord]]"
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stop_word: "MicroWakeWord"
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music_player: "MpvMediaPlayer"
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tts_player: "MpvMediaPlayer"
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wakeup_sound: str
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timer_finished_sound: str
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preferences: Preferences
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preferences_path: Path
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libtensorflowlite_c_path: Path
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event_bus: EventBus
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loop: asyncio.AbstractEventLoop
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oww_melspectrogram_path: Path
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oww_embedding_path: Path
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# --- Fields WITH default values ---
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satellite: "Optional[VoiceSatelliteProtocol]" = None
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wake_words_changed: bool = False
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refractory_seconds: float = 2.0
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mic_muted: bool = False
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def save_preferences(self) -> None:
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"""Save preferences as JSON."""
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_LOGGER.debug("Saving preferences: %s", self.preferences_path)
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self.preferences_path.parent.mkdir(parents=True, exist_ok=True)
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with open(self.preferences_path, "w", encoding="utf-8") as preferences_file:
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json.dump(
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asdict(self.preferences), preferences_file, ensure_ascii=False, indent=4
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)
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