Rename to Linux Voice Assistant
This commit is contained in:
Michael Hansen
@@ -0,0 +1,563 @@
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#!/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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import tempfile
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import shutil
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import posixpath
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from collections.abc import Iterable
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from dataclasses import dataclass, field
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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
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from urllib.request import urlopen
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from urllib.parse import urlparse, urlunparse
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# pylint: disable=no-name-in-module
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import sounddevice as sd
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from aioesphomeapi.api_pb2 import ( # type: ignore[attr-defined]
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DeviceInfoRequest,
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DeviceInfoResponse,
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ListEntitiesDoneResponse,
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ListEntitiesRequest,
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MediaPlayerCommandRequest,
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SubscribeHomeAssistantStatesRequest,
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VoiceAssistantAnnounceFinished,
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VoiceAssistantAnnounceRequest,
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VoiceAssistantAudio,
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VoiceAssistantConfigurationRequest,
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VoiceAssistantConfigurationResponse,
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VoiceAssistantEventResponse,
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VoiceAssistantRequest,
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VoiceAssistantSetConfiguration,
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VoiceAssistantTimerEventResponse,
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VoiceAssistantWakeWord,
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)
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from aioesphomeapi.model import (
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VoiceAssistantEventType,
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VoiceAssistantFeature,
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VoiceAssistantTimerEventType,
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)
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from google.protobuf import message
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from .api_server import APIServer
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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 .util import call_all, get_mac, is_arm
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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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_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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@dataclass
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class AvailableWakeWord:
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id: str
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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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url: Optional[str] = None
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@dataclass
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class ServerState:
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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_word: MicroWakeWord
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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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media_player_entity: Optional[MediaPlayerEntity] = None
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satellite: "Optional[VoiceSatelliteProtocol]" = None
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_external_wake_word_dir: Optional[tempfile.TemporaryDirectory] = None
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@property
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def external_wake_word_dir(self) -> Path:
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if self._external_wake_word_dir is None:
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self._external_wake_word_dir = tempfile.TemporaryDirectory()
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return Path(self._external_wake_word_dir.name)
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# -----------------------------------------------------------------------------
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class VoiceSatelliteProtocol(APIServer):
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def __init__(self, state: ServerState) -> None:
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super().__init__(state.name)
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self.state = state
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self.state.satellite = self
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if self.state.media_player_entity is None:
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self.state.media_player_entity = MediaPlayerEntity(
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server=self,
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key=len(state.entities),
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name="Media Player",
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object_id="linux_voice_assistant_media_player",
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music_player=state.music_player,
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announce_player=state.tts_player,
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)
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self.state.entities.append(self.state.media_player_entity)
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self._is_streaming_audio = False
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self._tts_url: Optional[str] = None
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self._tts_played = False
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self._continue_conversation = False
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self._timer_finished = False
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def handle_voice_event(
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self, event_type: VoiceAssistantEventType, data: Dict[str, str]
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) -> None:
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_LOGGER.debug("Voice event: type=%s, data=%s", event_type.name, data)
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if event_type == VoiceAssistantEventType.VOICE_ASSISTANT_RUN_START:
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self._tts_url = data.get("url")
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self._tts_played = False
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self._continue_conversation = False
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elif event_type in (
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VoiceAssistantEventType.VOICE_ASSISTANT_STT_VAD_END,
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VoiceAssistantEventType.VOICE_ASSISTANT_STT_END,
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):
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self._is_streaming_audio = False
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elif event_type == VoiceAssistantEventType.VOICE_ASSISTANT_INTENT_PROGRESS:
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if data.get("tts_start_streaming") == "1":
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# Start streaming early
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self.play_tts()
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elif event_type == VoiceAssistantEventType.VOICE_ASSISTANT_INTENT_END:
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if data.get("continue_conversation") == "1":
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self._continue_conversation = True
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elif event_type == VoiceAssistantEventType.VOICE_ASSISTANT_TTS_END:
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self._tts_url = data.get("url")
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self.play_tts()
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elif event_type == VoiceAssistantEventType.VOICE_ASSISTANT_RUN_END:
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self._is_streaming_audio = False
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if not self._tts_played:
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self._tts_finished()
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self._tts_played = False
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# TODO: handle error
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def handle_timer_event(
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self,
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event_type: VoiceAssistantTimerEventType,
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msg: VoiceAssistantTimerEventResponse,
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) -> None:
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_LOGGER.debug("Timer event: type=%s", event_type.name)
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if event_type == VoiceAssistantTimerEventType.VOICE_ASSISTANT_TIMER_FINISHED:
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if not self._timer_finished:
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self.state.stop_word.is_active = True
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self._timer_finished = True
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self.duck()
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self._play_timer_finished()
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def handle_message(self, msg: message.Message) -> Iterable[message.Message]:
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if isinstance(msg, VoiceAssistantEventResponse):
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# Pipeline event
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data: Dict[str, str] = {}
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for arg in msg.data:
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data[arg.name] = arg.value
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self.handle_voice_event(VoiceAssistantEventType(msg.event_type), data)
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elif isinstance(msg, VoiceAssistantAnnounceRequest):
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_LOGGER.debug("Announcing: %s", msg.text)
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assert self.state.media_player_entity is not None
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urls = []
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if msg.preannounce_media_id:
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urls.append(msg.preannounce_media_id)
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urls.append(msg.media_id)
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self.state.stop_word.is_active = True
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self._continue_conversation = msg.start_conversation
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self.duck()
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yield from self.state.media_player_entity.play(
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urls, announcement=True, done_callback=self._tts_finished
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)
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elif isinstance(msg, VoiceAssistantTimerEventResponse):
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self.handle_timer_event(VoiceAssistantTimerEventType(msg.event_type), msg)
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elif isinstance(msg, DeviceInfoRequest):
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yield DeviceInfoResponse(
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uses_password=False,
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name=self.state.name,
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mac_address=self.state.mac_address,
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voice_assistant_feature_flags=(
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VoiceAssistantFeature.VOICE_ASSISTANT
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| VoiceAssistantFeature.API_AUDIO
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| VoiceAssistantFeature.ANNOUNCE
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| VoiceAssistantFeature.START_CONVERSATION
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| VoiceAssistantFeature.TIMERS
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),
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)
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elif isinstance(
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msg,
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(
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ListEntitiesRequest,
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SubscribeHomeAssistantStatesRequest,
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MediaPlayerCommandRequest,
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),
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):
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for entity in self.state.entities:
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yield from entity.handle_message(msg)
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if isinstance(msg, ListEntitiesRequest):
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yield ListEntitiesDoneResponse()
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elif isinstance(msg, VoiceAssistantConfigurationRequest):
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external_wake_words = {
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external_ww.wake_word.id: external_ww
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for external_ww in msg.external_wake_words
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if external_ww.model_type == "micro_wake_word"
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}
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for wake_word_id, external_ww in external_wake_words.items():
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if wake_word_id in self.state.available_wake_words:
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# Skip if already added
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continue
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self.state.available_wake_words[wake_word_id] = AvailableWakeWord(
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id=external_ww.wake_word.id,
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wake_word=external_ww.wake_word.wake_word,
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trained_languages=external_ww.wake_word.trained_languages,
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config_path=self.state.external_wake_word_dir
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/ f"{external_ww.wake_word.id}.json",
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url=external_ww.url,
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)
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yield VoiceAssistantConfigurationResponse(
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available_wake_words=[
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VoiceAssistantWakeWord(
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id=ww.id,
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wake_word=ww.wake_word,
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trained_languages=ww.trained_languages,
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)
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for ww in self.state.available_wake_words.values()
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],
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active_wake_words=[self.state.wake_word.id],
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max_active_wake_words=1,
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)
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_LOGGER.info("Connected to Home Assistant")
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elif isinstance(msg, VoiceAssistantSetConfiguration):
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# TODO: support multiple wake words
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for wake_word_id in msg.active_wake_words:
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if wake_word_id == self.state.wake_word.id:
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# Already active
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break
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model_info = self.state.available_wake_words.get(wake_word_id)
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if not model_info:
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continue
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if (not model_info.config_path.exists()) and model_info.url:
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_LOGGER.debug("Downloading wake word config: %s", model_info.url)
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with (
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open(model_info.config_path, "wb") as config_file,
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urlopen(model_info.url) as response,
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):
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if response.status != 200:
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_LOGGER.error(
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"Download failed with status: %s", response.status
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)
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continue
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shutil.copyfileobj(response, config_file)
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with open(
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model_info.config_path, "r", encoding="utf-8"
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) as config_file:
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config_dict = json.load(config_file)
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parsed_config_url = urlparse(model_info.url)
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model_url = urlunparse(
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parsed_config_url._replace(
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path=posixpath.join(
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posixpath.dirname(parsed_config_url.path),
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config_dict["model"],
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)
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)
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)
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_LOGGER.debug("Downloading wake word model: %s", model_url)
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model_path = model_info.config_path.parent / config_dict["model"]
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with (
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open(model_path, "wb") as model_file,
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urlopen(model_url) as response,
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):
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if response.status != 200:
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_LOGGER.error(
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"Download failed with status: %s", response.status
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)
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continue
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shutil.copyfileobj(response, model_file)
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_LOGGER.debug("Loading wake word: %s", model_info.config_path)
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self.state.wake_word = MicroWakeWord.from_config(
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model_info.config_path,
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self.state.wake_word.libtensorflowlite_c_path,
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)
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_LOGGER.info("Wake word set: %s", self.state.wake_word.wake_word)
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break
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def handle_audio(self, audio_chunk: bytes) -> None:
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if not self._is_streaming_audio:
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return
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self.send_messages([VoiceAssistantAudio(data=audio_chunk)])
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def wakeup(self) -> None:
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if self._timer_finished:
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# Stop timer instead
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self._timer_finished = False
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self.state.tts_player.stop()
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_LOGGER.debug("Stopping timer finished sound")
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return
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wake_word_phrase = self.state.wake_word.wake_word
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_LOGGER.debug("Detected wake word: %s", wake_word_phrase)
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self.send_messages(
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[VoiceAssistantRequest(start=True, wake_word_phrase=wake_word_phrase)]
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)
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self.duck()
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self._is_streaming_audio = True
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self.state.tts_player.play(self.state.wakeup_sound)
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def stop(self) -> None:
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self.state.stop_word.is_active = False
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self.state.tts_player.stop()
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if self._timer_finished:
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self._timer_finished = False
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_LOGGER.debug("Stopping timer finished sound")
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else:
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_LOGGER.debug("TTS response stopped manually")
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self._tts_finished()
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def play_tts(self) -> None:
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if (not self._tts_url) or self._tts_played:
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return
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self._tts_played = True
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_LOGGER.debug("Playing TTS response: %s", self._tts_url)
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self.state.stop_word.is_active = True
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self.state.tts_player.play(self._tts_url, done_callback=self._tts_finished)
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def duck(self) -> None:
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_LOGGER.debug("Ducking music")
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self.state.music_player.duck()
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def unduck(self) -> None:
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_LOGGER.debug("Unducking music")
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self.state.music_player.unduck()
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def _tts_finished(self) -> None:
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self.state.stop_word.is_active = False
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self.send_messages([VoiceAssistantAnnounceFinished()])
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if self._continue_conversation:
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self.send_messages([VoiceAssistantRequest(start=True)])
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self._is_streaming_audio = True
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_LOGGER.debug("Continuing conversation")
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else:
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self.unduck()
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_LOGGER.debug("TTS response finished")
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def _play_timer_finished(self) -> None:
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if not self._timer_finished:
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self.unduck()
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return
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self.state.tts_player.play(
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self.state.timer_finished_sound,
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done_callback=lambda: call_all(
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lambda: time.sleep(1.0), self._play_timer_finished
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),
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)
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def connection_lost(self, exc):
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super().connection_lost(exc)
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_LOGGER.info("Disconnected from Home Assistant")
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def process_audio(state: ServerState):
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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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try:
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state.satellite.handle_audio(audio_chunk)
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if state.wake_word.is_active and state.wake_word.process_streaming(
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audio_chunk
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):
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state.satellite.wakeup()
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if state.stop_word.is_active and state.stop_word.process_streaming(
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audio_chunk
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):
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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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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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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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"--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(
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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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"--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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# Load available wake words
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wake_word_dir = Path(args.wake_word_dir)
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available_wake_words: Dict[str, AvailableWakeWord] = {}
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for model_config_path in wake_word_dir.glob("*.json"):
|
||||
model_id = model_config_path.stem
|
||||
if model_id == args.stop_model:
|
||||
# Don't show stop model as an available wake word
|
||||
continue
|
||||
|
||||
with open(model_config_path, "r", encoding="utf-8") as model_config_file:
|
||||
model_config = json.load(model_config_file)
|
||||
available_wake_words[model_id] = AvailableWakeWord(
|
||||
id=model_id,
|
||||
wake_word=model_config["wake_word"],
|
||||
trained_languages=model_config.get("trained_languages", []),
|
||||
config_path=model_config_path,
|
||||
)
|
||||
|
||||
_LOGGER.debug("Available wake words: %s", list(sorted(available_wake_words.keys())))
|
||||
|
||||
libtensorflowlite_c_path = _LIB_DIR / "libtensorflowlite_c.so"
|
||||
_LOGGER.debug("libtensorflowlite_c path: %s", libtensorflowlite_c_path)
|
||||
|
||||
# Load wake/stop models
|
||||
wake_config_path = wake_word_dir / f"{args.wake_model}.json"
|
||||
_LOGGER.debug("Loading wake model: %s", wake_config_path)
|
||||
wake_model = MicroWakeWord.from_config(wake_config_path, libtensorflowlite_c_path)
|
||||
|
||||
stop_config_path = wake_word_dir / f"{args.stop_model}.json"
|
||||
_LOGGER.debug("Loading stop model: %s", stop_config_path)
|
||||
stop_model = MicroWakeWord.from_config(stop_config_path, libtensorflowlite_c_path)
|
||||
|
||||
state = ServerState(
|
||||
name=args.name,
|
||||
mac_address=get_mac(),
|
||||
audio_queue=Queue(),
|
||||
entities=[],
|
||||
available_wake_words=available_wake_words,
|
||||
wake_word=wake_model,
|
||||
stop_word=stop_model,
|
||||
music_player=MpvMediaPlayer(device=args.audio_output_device),
|
||||
tts_player=MpvMediaPlayer(device=args.audio_output_device),
|
||||
wakeup_sound=args.wakeup_sound,
|
||||
timer_finished_sound=args.timer_finished_sound,
|
||||
)
|
||||
|
||||
process_audio_thread = threading.Thread(
|
||||
target=process_audio, args=(state,), daemon=True
|
||||
)
|
||||
process_audio_thread.start()
|
||||
|
||||
def sd_callback(indata, _frames, _time, _status):
|
||||
state.audio_queue.put_nowait(bytes(indata))
|
||||
|
||||
loop = asyncio.get_running_loop()
|
||||
server = await loop.create_server(
|
||||
lambda: VoiceSatelliteProtocol(state), host=args.host, port=args.port
|
||||
)
|
||||
|
||||
try:
|
||||
_LOGGER.debug("Opening audio input device: %s", args.audio_input_device)
|
||||
with sd.RawInputStream(
|
||||
samplerate=16000,
|
||||
blocksize=args.audio_input_block_size,
|
||||
device=args.audio_input_device,
|
||||
dtype="int16",
|
||||
channels=1,
|
||||
callback=sd_callback,
|
||||
):
|
||||
async with server:
|
||||
_LOGGER.info("Server started (host=%s, port=%s)", args.host, args.port)
|
||||
await server.serve_forever()
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
finally:
|
||||
state.audio_queue.put_nowait(None)
|
||||
process_audio_thread.join()
|
||||
|
||||
_LOGGER.debug("Server stopped")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,180 @@
|
||||
"""Partial ESPHome server implementation."""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from abc import abstractmethod
|
||||
from collections.abc import Iterable
|
||||
from typing import TYPE_CHECKING, List, Optional
|
||||
|
||||
# pylint: disable=no-name-in-module
|
||||
from aioesphomeapi._frame_helper.packets import make_plain_text_packets
|
||||
from aioesphomeapi.api_pb2 import ( # type: ignore[attr-defined]
|
||||
ConnectRequest,
|
||||
ConnectResponse,
|
||||
DisconnectRequest,
|
||||
DisconnectResponse,
|
||||
HelloRequest,
|
||||
HelloResponse,
|
||||
PingRequest,
|
||||
PingResponse,
|
||||
)
|
||||
from aioesphomeapi.core import MESSAGE_TYPE_TO_PROTO
|
||||
from google.protobuf import message
|
||||
|
||||
PROTO_TO_MESSAGE_TYPE = {v: k for k, v in MESSAGE_TYPE_TO_PROTO.items()}
|
||||
|
||||
_LOGGER = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class APIServer(asyncio.Protocol):
|
||||
|
||||
def __init__(self, name: str) -> None:
|
||||
self.name = name
|
||||
|
||||
self._buffer: Optional[bytes] = None
|
||||
self._buffer_len: int = 0
|
||||
self._pos: int = 0
|
||||
self._transport = None
|
||||
self._writelines = None
|
||||
|
||||
@abstractmethod
|
||||
def handle_message(self, msg: message.Message) -> Iterable[message.Message]:
|
||||
pass
|
||||
|
||||
def process_packet(self, msg_type: int, packet_data: bytes) -> None:
|
||||
msg_class = MESSAGE_TYPE_TO_PROTO[msg_type]
|
||||
msg_inst = msg_class.FromString(packet_data)
|
||||
|
||||
if isinstance(msg_inst, HelloRequest):
|
||||
self.send_messages(
|
||||
[
|
||||
HelloResponse(
|
||||
api_version_major=1,
|
||||
api_version_minor=10,
|
||||
name=self.name,
|
||||
)
|
||||
]
|
||||
)
|
||||
return
|
||||
|
||||
if isinstance(msg_inst, ConnectRequest):
|
||||
self.send_messages([ConnectResponse()])
|
||||
elif isinstance(msg_inst, DisconnectRequest):
|
||||
self.send_messages([DisconnectResponse()])
|
||||
_LOGGER.debug("Disconnect requested")
|
||||
if self._transport:
|
||||
self._transport.close()
|
||||
self._transport = None
|
||||
self._writelines = None
|
||||
elif isinstance(msg_inst, PingRequest):
|
||||
self.send_messages([PingResponse()])
|
||||
elif msgs := self.handle_message(msg_inst):
|
||||
if isinstance(msgs, message.Message):
|
||||
msgs = [msgs]
|
||||
|
||||
self.send_messages(msgs)
|
||||
|
||||
def send_messages(self, msgs: List[message.Message]):
|
||||
if self._writelines is None:
|
||||
return
|
||||
|
||||
packets = [
|
||||
(PROTO_TO_MESSAGE_TYPE[msg.__class__], msg.SerializeToString())
|
||||
for msg in msgs
|
||||
]
|
||||
packet_bytes = make_plain_text_packets(packets)
|
||||
self._writelines(packet_bytes)
|
||||
|
||||
def connection_made(self, transport) -> None:
|
||||
self._transport = transport
|
||||
self._writelines = transport.writelines
|
||||
|
||||
def data_received(self, data: bytes):
|
||||
if self._buffer is None:
|
||||
self._buffer = data
|
||||
self._buffer_len = len(data)
|
||||
else:
|
||||
self._buffer += data
|
||||
self._buffer_len += len(data)
|
||||
|
||||
while self._buffer_len >= 3:
|
||||
self._pos = 0
|
||||
# Read preamble, which should always 0x00
|
||||
if (preamble := self._read_varuint()) != 0x00:
|
||||
_LOGGER.error("Incorrect preamble: %s", preamble)
|
||||
return
|
||||
|
||||
if (length := self._read_varuint()) == -1:
|
||||
_LOGGER.error("Incorrect length: %s", length)
|
||||
return
|
||||
|
||||
if (msg_type := self._read_varuint()) == -1:
|
||||
_LOGGER.error("Incorrect message type: %s", msg_type)
|
||||
return
|
||||
|
||||
if length == 0:
|
||||
# Empty message (allowed)
|
||||
self._remove_from_buffer()
|
||||
self.process_packet(msg_type, b"")
|
||||
continue
|
||||
|
||||
if (packet_data := self._read(length)) is None:
|
||||
return
|
||||
|
||||
self._remove_from_buffer()
|
||||
self.process_packet(msg_type, packet_data)
|
||||
|
||||
def _read(self, length: int) -> bytes | None:
|
||||
"""Read exactly length bytes from the buffer or None if all the bytes are not yet available."""
|
||||
new_pos = self._pos + length
|
||||
if self._buffer_len < new_pos:
|
||||
return None
|
||||
original_pos = self._pos
|
||||
self._pos = new_pos
|
||||
if TYPE_CHECKING:
|
||||
assert self._buffer is not None, "Buffer should be set"
|
||||
cstr = self._buffer
|
||||
# Important: we must keep the bounds check (self._buffer_len < new_pos)
|
||||
# above to verify we never try to read past the end of the buffer
|
||||
return cstr[original_pos:new_pos]
|
||||
|
||||
def connection_lost(self, exc):
|
||||
self._transport = None
|
||||
self._writelines = None
|
||||
|
||||
def _read_varuint(self) -> int:
|
||||
"""Read a varuint from the buffer or -1 if the buffer runs out of bytes."""
|
||||
if not self._buffer:
|
||||
return -1
|
||||
|
||||
result = 0
|
||||
bitpos = 0
|
||||
cstr = self._buffer
|
||||
while self._buffer_len > self._pos:
|
||||
val = cstr[self._pos]
|
||||
self._pos += 1
|
||||
result |= (val & 0x7F) << bitpos
|
||||
if (val & 0x80) == 0:
|
||||
return result
|
||||
bitpos += 7
|
||||
return -1
|
||||
|
||||
def _remove_from_buffer(self) -> None:
|
||||
"""Remove data from the buffer."""
|
||||
end_of_frame_pos = self._pos
|
||||
self._buffer_len -= end_of_frame_pos
|
||||
if self._buffer_len == 0:
|
||||
# This is the best case scenario, we can just set the buffer to None
|
||||
# and don't have to copy the data. This is the most common case as well.
|
||||
self._buffer = None
|
||||
return
|
||||
if TYPE_CHECKING:
|
||||
assert self._buffer is not None, "Buffer should be set"
|
||||
# This is the worst case scenario, we have to copy the data
|
||||
# and can't just use the buffer directly. This should only happen
|
||||
# when we read multiple frames at once because the event loop
|
||||
# is blocked and we cannot pull the data out of the buffer fast enough.
|
||||
cstr = self._buffer
|
||||
# Important: we must use the explicit length for the slice
|
||||
# since Cython will stop at any '\0' character if we don't
|
||||
self._buffer = cstr[end_of_frame_pos : self._buffer_len + end_of_frame_pos]
|
||||
@@ -0,0 +1,133 @@
|
||||
from abc import abstractmethod
|
||||
from collections.abc import Iterable
|
||||
from typing import Callable, List, Optional, Union
|
||||
|
||||
# pylint: disable=no-name-in-module
|
||||
from aioesphomeapi.api_pb2 import ( # type: ignore[attr-defined]
|
||||
ListEntitiesMediaPlayerResponse,
|
||||
ListEntitiesRequest,
|
||||
MediaPlayerCommandRequest,
|
||||
MediaPlayerStateResponse,
|
||||
SubscribeHomeAssistantStatesRequest,
|
||||
)
|
||||
from aioesphomeapi.model import MediaPlayerCommand, MediaPlayerState
|
||||
from google.protobuf import message
|
||||
|
||||
from .api_server import APIServer
|
||||
from .mpv_player import MpvMediaPlayer
|
||||
from .util import call_all
|
||||
|
||||
|
||||
class ESPHomeEntity:
|
||||
def __init__(self, server: APIServer) -> None:
|
||||
self.server = server
|
||||
|
||||
@abstractmethod
|
||||
def handle_message(self, msg: message.Message) -> Iterable[message.Message]:
|
||||
pass
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class MediaPlayerEntity(ESPHomeEntity):
|
||||
def __init__(
|
||||
self,
|
||||
server: APIServer,
|
||||
key: int,
|
||||
name: str,
|
||||
object_id: str,
|
||||
music_player: MpvMediaPlayer,
|
||||
announce_player: MpvMediaPlayer,
|
||||
) -> None:
|
||||
ESPHomeEntity.__init__(self, server)
|
||||
|
||||
self.key = key
|
||||
self.name = name
|
||||
self.object_id = object_id
|
||||
self.state = MediaPlayerState.IDLE
|
||||
self.volume = 1.0
|
||||
self.muted = False
|
||||
self.music_player = music_player
|
||||
self.announce_player = announce_player
|
||||
|
||||
def play(
|
||||
self,
|
||||
url: Union[str, List[str]],
|
||||
announcement: bool = False,
|
||||
done_callback: Optional[Callable[[], None]] = None,
|
||||
) -> Iterable[message.Message]:
|
||||
if announcement:
|
||||
if self.music_player.is_playing:
|
||||
# Announce, resume music
|
||||
self.music_player.pause()
|
||||
self.announce_player.play(
|
||||
url,
|
||||
done_callback=lambda: call_all(
|
||||
self.music_player.resume, done_callback
|
||||
),
|
||||
)
|
||||
else:
|
||||
# Announce, idle
|
||||
self.announce_player.play(
|
||||
url,
|
||||
done_callback=lambda: call_all(
|
||||
self.server.send_messages(
|
||||
[self._update_state(MediaPlayerState.IDLE)]
|
||||
),
|
||||
done_callback,
|
||||
),
|
||||
)
|
||||
else:
|
||||
# Music
|
||||
self.music_player.play(
|
||||
url,
|
||||
done_callback=lambda: call_all(
|
||||
self.server.send_messages(
|
||||
[self._update_state(MediaPlayerState.IDLE)]
|
||||
),
|
||||
done_callback,
|
||||
),
|
||||
)
|
||||
|
||||
yield self._update_state(MediaPlayerState.PLAYING)
|
||||
|
||||
def handle_message(self, msg: message.Message) -> Iterable[message.Message]:
|
||||
if isinstance(msg, MediaPlayerCommandRequest) and (msg.key == self.key):
|
||||
if msg.has_media_url:
|
||||
announcement = msg.has_announcement and msg.announcement
|
||||
yield from self.play(msg.media_url, announcement=announcement)
|
||||
elif msg.has_command:
|
||||
if msg.command == MediaPlayerCommand.PAUSE:
|
||||
self.music_player.pause()
|
||||
yield self._update_state(MediaPlayerState.PAUSED)
|
||||
elif msg.command == MediaPlayerCommand.PLAY:
|
||||
self.music_player.resume()
|
||||
yield self._update_state(MediaPlayerState.PLAYING)
|
||||
elif msg.has_volume:
|
||||
volume = int(msg.volume * 100)
|
||||
self.music_player.set_volume(volume)
|
||||
self.announce_player.set_volume(volume)
|
||||
self.volume = msg.volume
|
||||
yield self._update_state(self.state)
|
||||
elif isinstance(msg, ListEntitiesRequest):
|
||||
yield ListEntitiesMediaPlayerResponse(
|
||||
object_id=self.object_id,
|
||||
key=self.key,
|
||||
name=self.name,
|
||||
supports_pause=True,
|
||||
)
|
||||
elif isinstance(msg, SubscribeHomeAssistantStatesRequest):
|
||||
yield self._get_state_message()
|
||||
|
||||
def _update_state(self, new_state: MediaPlayerState) -> MediaPlayerStateResponse:
|
||||
self.state = new_state
|
||||
return self._get_state_message()
|
||||
|
||||
def _get_state_message(self) -> MediaPlayerStateResponse:
|
||||
return MediaPlayerStateResponse(
|
||||
key=self.key,
|
||||
state=self.state,
|
||||
volume=self.volume,
|
||||
muted=self.muted,
|
||||
)
|
||||
@@ -0,0 +1,272 @@
|
||||
import ctypes
|
||||
import json
|
||||
import statistics
|
||||
from collections import deque
|
||||
from pathlib import Path
|
||||
from typing import Deque, List, Union
|
||||
|
||||
import numpy as np
|
||||
from pymicro_features import MicroFrontend
|
||||
|
||||
_SAMPLES_PER_SECOND = 16000
|
||||
_SAMPLES_PER_CHUNK = 160 # 10ms
|
||||
_BYTES_PER_SAMPLE = 2 # 16-bit
|
||||
_BYTES_PER_CHUNK = _SAMPLES_PER_CHUNK * _BYTES_PER_SAMPLE
|
||||
_SECONDS_PER_CHUNK = _SAMPLES_PER_CHUNK / _SAMPLES_PER_SECOND
|
||||
_STRIDE = 3
|
||||
_DEFAULT_REFRACTORY = 2 # seconds
|
||||
|
||||
|
||||
class TfLiteQuantizationParams(ctypes.Structure):
|
||||
_fields_ = [("scale", ctypes.c_float), ("zero_point", ctypes.c_int32)]
|
||||
|
||||
|
||||
class MicroWakeWord:
|
||||
def __init__(
|
||||
self,
|
||||
id: str, # pylint: disable=redefined-builtin
|
||||
wake_word: str,
|
||||
tflite_model: Union[str, Path],
|
||||
probability_cutoff: float,
|
||||
sliding_window_size: int,
|
||||
refractory_seconds: float,
|
||||
trained_languages: List[str],
|
||||
libtensorflowlite_c_path: Union[str, Path],
|
||||
):
|
||||
self.id = id
|
||||
self.wake_word = wake_word
|
||||
self.tflite_model = tflite_model
|
||||
self.probability_cutoff = probability_cutoff
|
||||
self.sliding_window_size = sliding_window_size
|
||||
self.refractory_seconds = refractory_seconds
|
||||
self.trained_languages = trained_languages
|
||||
|
||||
self.is_active = True
|
||||
|
||||
# Load the shared library
|
||||
self.libtensorflowlite_c_path = Path(libtensorflowlite_c_path)
|
||||
self.lib = ctypes.cdll.LoadLibrary(str(self.libtensorflowlite_c_path.resolve()))
|
||||
|
||||
# Define required argument/return types for C API
|
||||
self.lib.TfLiteModelCreateFromFile.argtypes = [ctypes.c_char_p]
|
||||
self.lib.TfLiteModelCreateFromFile.restype = ctypes.c_void_p
|
||||
|
||||
self.lib.TfLiteInterpreterCreate.argtypes = [ctypes.c_void_p, ctypes.c_void_p]
|
||||
self.lib.TfLiteInterpreterCreate.restype = ctypes.c_void_p
|
||||
|
||||
self.lib.TfLiteInterpreterAllocateTensors.argtypes = [ctypes.c_void_p]
|
||||
self.lib.TfLiteInterpreterInvoke.argtypes = [ctypes.c_void_p]
|
||||
|
||||
self.lib.TfLiteInterpreterGetInputTensor.argtypes = [
|
||||
ctypes.c_void_p,
|
||||
ctypes.c_int,
|
||||
]
|
||||
self.lib.TfLiteInterpreterGetInputTensor.restype = ctypes.c_void_p
|
||||
|
||||
self.lib.TfLiteInterpreterGetOutputTensor.argtypes = [
|
||||
ctypes.c_void_p,
|
||||
ctypes.c_int,
|
||||
]
|
||||
self.lib.TfLiteInterpreterGetOutputTensor.restype = ctypes.c_void_p
|
||||
|
||||
self.lib.TfLiteTensorCopyFromBuffer.argtypes = [
|
||||
ctypes.c_void_p,
|
||||
ctypes.c_void_p,
|
||||
ctypes.c_size_t,
|
||||
]
|
||||
self.lib.TfLiteTensorCopyToBuffer.argtypes = [
|
||||
ctypes.c_void_p,
|
||||
ctypes.c_void_p,
|
||||
ctypes.c_size_t,
|
||||
]
|
||||
|
||||
self.lib.TfLiteTensorType.restype = ctypes.c_int
|
||||
|
||||
self.lib.TfLiteTensorByteSize.argtypes = [ctypes.c_void_p]
|
||||
self.lib.TfLiteTensorByteSize.restype = ctypes.c_size_t
|
||||
|
||||
self.lib.TfLiteTensorQuantizationParams.restype = ctypes.Structure
|
||||
|
||||
self.lib.TfLiteTensorQuantizationParams.argtypes = [ctypes.c_void_p]
|
||||
self.lib.TfLiteTensorQuantizationParams.restype = TfLiteQuantizationParams
|
||||
|
||||
# Load the model and create interpreter
|
||||
self.model_path = str(Path(tflite_model).resolve()).encode("utf-8")
|
||||
self._load_model()
|
||||
|
||||
self._frontend = MicroFrontend()
|
||||
self._features: List[np.ndarray] = []
|
||||
self._probabilities: Deque[float] = deque(maxlen=self.sliding_window_size)
|
||||
self._audio_buffer = bytes()
|
||||
self._ignore_seconds: float = 0
|
||||
|
||||
def _load_model(self) -> None:
|
||||
self.model = self.lib.TfLiteModelCreateFromFile(self.model_path)
|
||||
self.interpreter = self.lib.TfLiteInterpreterCreate(self.model, None)
|
||||
self.lib.TfLiteInterpreterAllocateTensors(self.interpreter)
|
||||
|
||||
# Access input and output tensor
|
||||
self.input_tensor = self.lib.TfLiteInterpreterGetInputTensor(
|
||||
self.interpreter, 0
|
||||
)
|
||||
self.output_tensor = self.lib.TfLiteInterpreterGetOutputTensor(
|
||||
self.interpreter, 0
|
||||
)
|
||||
|
||||
# Get quantization parameters
|
||||
input_q = self.lib.TfLiteTensorQuantizationParams(self.input_tensor)
|
||||
output_q = self.lib.TfLiteTensorQuantizationParams(self.output_tensor)
|
||||
|
||||
self.input_scale, self.input_zero_point = input_q.scale, input_q.zero_point
|
||||
self.output_scale, self.output_zero_point = output_q.scale, output_q.zero_point
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Reload model and clear state.
|
||||
|
||||
This must be done between audio clips when not streaming.
|
||||
"""
|
||||
self._audio_buffer = bytes()
|
||||
self._features.clear()
|
||||
self._probabilities.clear()
|
||||
self._ignore_seconds = 0
|
||||
|
||||
# Clear out residual features
|
||||
self._frontend = MicroFrontend()
|
||||
|
||||
# Need to reload model to reset intermediary results
|
||||
# reset_all_variables() doesn't work.
|
||||
self._load_model()
|
||||
|
||||
@property
|
||||
def samples_per_chunk(self) -> int:
|
||||
"""Number of samples in a streaming audio chunk."""
|
||||
return _SAMPLES_PER_CHUNK
|
||||
|
||||
@property
|
||||
def bytes_per_chunk(self) -> int:
|
||||
"""Number of bytes in a streaming audio chunk.
|
||||
|
||||
Assumes 16-bit mono samples at 16Khz.
|
||||
"""
|
||||
return _BYTES_PER_CHUNK
|
||||
|
||||
@staticmethod
|
||||
def from_config(
|
||||
config_path: Union[str, Path],
|
||||
libtensorflowlite_c_path: Union[str, Path],
|
||||
refractory_seconds: float = _DEFAULT_REFRACTORY,
|
||||
) -> "MicroWakeWord":
|
||||
"""Load a microWakeWord model from a JSON config file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
config_path: str or Path
|
||||
Path to JSON configuration file
|
||||
refractory_seconds: float
|
||||
Number of seconds to ignore after detection
|
||||
"""
|
||||
config_path = Path(config_path)
|
||||
with open(config_path, "r", encoding="utf-8") as config_file:
|
||||
config = json.load(config_file)
|
||||
|
||||
micro_config = config["micro"]
|
||||
|
||||
return MicroWakeWord(
|
||||
id=Path(config["model"]).stem,
|
||||
wake_word=config["wake_word"],
|
||||
tflite_model=config_path.parent / config["model"],
|
||||
probability_cutoff=micro_config["probability_cutoff"],
|
||||
sliding_window_size=micro_config["sliding_window_size"],
|
||||
refractory_seconds=refractory_seconds,
|
||||
trained_languages=micro_config.get("trained_languages", []),
|
||||
libtensorflowlite_c_path=libtensorflowlite_c_path,
|
||||
)
|
||||
|
||||
def process_streaming(self, audio_bytes: bytes) -> bool:
|
||||
"""Process a chunk of audio in streaming mode.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
audio_bytes: bytes
|
||||
Raw 16-bit mono audio samples at 16Khz
|
||||
|
||||
Returns True if the wake word was detected.
|
||||
"""
|
||||
self._audio_buffer += audio_bytes
|
||||
|
||||
if len(self._audio_buffer) < _BYTES_PER_CHUNK:
|
||||
# Not enough audio to get features
|
||||
return False
|
||||
|
||||
detected = False
|
||||
audio_buffer_idx = 0
|
||||
while (audio_buffer_idx + _BYTES_PER_CHUNK) <= len(self._audio_buffer):
|
||||
# Process chunk
|
||||
chunk_bytes = self._audio_buffer[
|
||||
audio_buffer_idx : audio_buffer_idx + _BYTES_PER_CHUNK
|
||||
]
|
||||
frontend_result = self._frontend.ProcessSamples(chunk_bytes)
|
||||
audio_buffer_idx += frontend_result.samples_read * _BYTES_PER_SAMPLE
|
||||
self._ignore_seconds = max(0, self._ignore_seconds - _SECONDS_PER_CHUNK)
|
||||
|
||||
if not frontend_result.features:
|
||||
# Not enough audio for a full window
|
||||
continue
|
||||
|
||||
self._features.append(
|
||||
np.array(frontend_result.features).reshape(
|
||||
(1, 1, len(frontend_result.features))
|
||||
)
|
||||
)
|
||||
|
||||
if len(self._features) < _STRIDE:
|
||||
# Not enough windows
|
||||
continue
|
||||
|
||||
# Allocate and quantize input data
|
||||
quant_features = np.round(
|
||||
np.concatenate(self._features, axis=1) / self.input_scale
|
||||
+ self.input_zero_point
|
||||
).astype(np.uint8)
|
||||
|
||||
# Stride instead of rolling
|
||||
self._features.clear()
|
||||
|
||||
# Set tensor
|
||||
quant_ptr = quant_features.ctypes.data_as(ctypes.c_void_p)
|
||||
self.lib.TfLiteTensorCopyFromBuffer(
|
||||
self.input_tensor, quant_ptr, quant_features.nbytes
|
||||
)
|
||||
|
||||
# Run inference
|
||||
self.lib.TfLiteInterpreterInvoke(self.interpreter)
|
||||
|
||||
# Read output
|
||||
output_bytes = self.lib.TfLiteTensorByteSize(self.output_tensor)
|
||||
output_data = np.empty(output_bytes, dtype=np.uint8)
|
||||
self.lib.TfLiteTensorCopyToBuffer(
|
||||
self.output_tensor,
|
||||
output_data.ctypes.data_as(ctypes.c_void_p),
|
||||
output_bytes,
|
||||
)
|
||||
|
||||
# Dequantize output
|
||||
result = (
|
||||
output_data.astype(np.float32) - self.output_zero_point
|
||||
) * self.output_scale
|
||||
|
||||
self._probabilities.append(result.item())
|
||||
|
||||
if len(self._probabilities) < self.sliding_window_size:
|
||||
# Not enough probabilities
|
||||
continue
|
||||
|
||||
if statistics.mean(self._probabilities) > self.probability_cutoff:
|
||||
if self._ignore_seconds <= 0:
|
||||
detected = True
|
||||
self._ignore_seconds = self.refractory_seconds
|
||||
|
||||
# Remove processed audio
|
||||
self._audio_buffer = self._audio_buffer[audio_buffer_idx:]
|
||||
|
||||
return detected
|
||||
@@ -0,0 +1,94 @@
|
||||
"""Media player using mpv in a subprocess."""
|
||||
|
||||
import logging
|
||||
from collections.abc import Callable
|
||||
from threading import Lock
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from mpv import MPV
|
||||
|
||||
_LOGGER = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MpvMediaPlayer:
|
||||
def __init__(self, device: Optional[str] = None) -> None:
|
||||
self.player = MPV()
|
||||
|
||||
if device:
|
||||
self.player["audio-device"] = device
|
||||
|
||||
self.is_playing = False
|
||||
|
||||
self._playlist: List[str] = []
|
||||
self._done_callback: Optional[Callable[[], None]] = None
|
||||
self._done_callback_lock = Lock()
|
||||
|
||||
self._duck_volume: int = 50
|
||||
self._unduck_volume: int = 100
|
||||
|
||||
self.player.event_callback("end-file")(self._on_end_file)
|
||||
|
||||
def play(
|
||||
self,
|
||||
url: Union[str, List[str]],
|
||||
done_callback: Optional[Callable[[], None]] = None,
|
||||
stop_first: bool = True,
|
||||
) -> None:
|
||||
self.stop()
|
||||
|
||||
if isinstance(url, str):
|
||||
self._playlist = [url]
|
||||
else:
|
||||
self._playlist = url
|
||||
|
||||
next_url = self._playlist.pop(0)
|
||||
_LOGGER.debug("Playing %s", next_url)
|
||||
|
||||
self._done_callback = done_callback
|
||||
self.is_playing = True
|
||||
self.player.play(next_url)
|
||||
|
||||
def pause(self) -> None:
|
||||
self.player.pause = True
|
||||
self.is_playing = False
|
||||
|
||||
def resume(self) -> None:
|
||||
self.player.pause = False
|
||||
if self._playlist:
|
||||
self.is_playing = True
|
||||
|
||||
def stop(self) -> None:
|
||||
self.player.stop()
|
||||
self._playlist.clear()
|
||||
|
||||
def duck(self) -> None:
|
||||
self.player.volume = self._duck_volume
|
||||
|
||||
def unduck(self) -> None:
|
||||
self.player.volume = self._unduck_volume
|
||||
|
||||
def set_volume(self, volume: int) -> None:
|
||||
volume = max(0, min(100, volume))
|
||||
self.player.volume = volume
|
||||
|
||||
self._unduck_volume = volume
|
||||
self._duck_volume = volume // 2
|
||||
|
||||
def _on_end_file(self, event) -> None:
|
||||
if self._playlist:
|
||||
self.player.play(self._playlist.pop(0))
|
||||
return
|
||||
|
||||
self.is_playing = False
|
||||
|
||||
todo_callback: Optional[Callable[[], None]] = None
|
||||
with self._done_callback_lock:
|
||||
if self._done_callback:
|
||||
todo_callback = self._done_callback
|
||||
self._done_callback = None
|
||||
|
||||
if todo_callback:
|
||||
try:
|
||||
todo_callback()
|
||||
except Exception:
|
||||
_LOGGER.exception("Unexpected error running done callback")
|
||||
@@ -0,0 +1,22 @@
|
||||
"""Utility methods."""
|
||||
|
||||
import platform
|
||||
import uuid
|
||||
from collections.abc import Callable
|
||||
from typing import Optional
|
||||
|
||||
|
||||
def get_mac() -> str:
|
||||
mac = uuid.getnode()
|
||||
mac_str = ":".join(f"{(mac >> i) & 0xff:02x}" for i in range(40, -1, -8))
|
||||
return mac_str
|
||||
|
||||
|
||||
def call_all(*callables: Optional[Callable[[], None]]) -> None:
|
||||
for item in filter(None, callables):
|
||||
item()
|
||||
|
||||
|
||||
def is_arm() -> bool:
|
||||
machine = platform.machine()
|
||||
return ("arm" in machine) or ("aarch" in machine)
|
||||
Reference in New Issue
Block a user