"""Classes for voice assistant pipelines."""
from __future__ import annotations
import array
import asyncio
from collections import defaultdict, deque
from collections.abc import AsyncGenerator, AsyncIterable, Callable, Iterable
from dataclasses import asdict, dataclass, field
from enum import StrEnum
import logging
from pathlib import Path
from queue import Empty, Queue
from threading import Thread
import time
from typing import TYPE_CHECKING, Any, Final, cast
import wave
import voluptuous as vol
if TYPE_CHECKING:
from webrtc_noise_gain import AudioProcessor
from homeassistant.components import (
conversation,
media_source,
stt,
tts,
wake_word,
websocket_api,
)
from homeassistant.components.tts.media_source import (
generate_media_source_id as tts_generate_media_source_id,
)
from homeassistant.core import Context, HomeAssistant, callback
from homeassistant.exceptions import HomeAssistantError
from homeassistant.helpers.collection import (
CHANGE_UPDATED,
CollectionError,
ItemNotFound,
SerializedStorageCollection,
StorageCollection,
StorageCollectionWebsocket,
)
from homeassistant.helpers.singleton import singleton
from homeassistant.helpers.storage import Store
from homeassistant.helpers.typing import UNDEFINED, UndefinedType
from homeassistant.util import (
dt as dt_util,
language as language_util,
ulid as ulid_util,
)
from homeassistant.util.limited_size_dict import LimitedSizeDict
from .const import (
CONF_DEBUG_RECORDING_DIR,
DATA_CONFIG,
DATA_LAST_WAKE_UP,
DEFAULT_WAKE_WORD_COOLDOWN,
DOMAIN,
)
from .error import (
IntentRecognitionError,
PipelineError,
PipelineNotFound,
SpeechToTextError,
TextToSpeechError,
WakeWordDetectionAborted,
WakeWordDetectionError,
WakeWordTimeoutError,
)
from .vad import AudioBuffer, VoiceActivityTimeout, VoiceCommandSegmenter, chunk_samples
_LOGGER = logging.getLogger(__name__)
STORAGE_KEY = f"{DOMAIN}.pipelines"
STORAGE_VERSION = 1
STORAGE_VERSION_MINOR = 2
ENGINE_LANGUAGE_PAIRS = (
("stt_engine", "stt_language"),
("tts_engine", "tts_language"),
)
def validate_language(data: dict[str, Any]) -> Any:
"""Validate language settings."""
for engine, language in ENGINE_LANGUAGE_PAIRS:
if data[engine] is not None and data[language] is None:
raise vol.Invalid(f"Need language {language} for {engine} {data[engine]}")
return data
PIPELINE_FIELDS = {
vol.Required("conversation_engine"): str,
vol.Required("conversation_language"): str,
vol.Required("language"): str,
vol.Required("name"): str,
vol.Required("stt_engine"): vol.Any(str, None),
vol.Required("stt_language"): vol.Any(str, None),
vol.Required("tts_engine"): vol.Any(str, None),
vol.Required("tts_language"): vol.Any(str, None),
vol.Required("tts_voice"): vol.Any(str, None),
vol.Required("wake_word_entity"): vol.Any(str, None),
vol.Required("wake_word_id"): vol.Any(str, None),
}
STORED_PIPELINE_RUNS = 10
SAVE_DELAY = 10
AUDIO_PROCESSOR_SAMPLES: Final = 160 # 10 ms @ 16 Khz
AUDIO_PROCESSOR_BYTES: Final = AUDIO_PROCESSOR_SAMPLES * 2 # 16-bit samples
async def _async_resolve_default_pipeline_settings(
hass: HomeAssistant,
stt_engine_id: str | None,
tts_engine_id: str | None,
pipeline_name: str,
) -> dict[str, str | None]:
"""Resolve settings for a default pipeline.
The default pipeline will use the homeassistant conversation agent and the
default stt / tts engines if none are specified.
"""
conversation_language = "en"
pipeline_language = "en"
stt_engine = None
stt_language = None
tts_engine = None
tts_language = None
tts_voice = None
wake_word_entity = None
wake_word_id = None
# Find a matching language supported by the Home Assistant conversation agent
conversation_languages = language_util.matches(
hass.config.language,
await conversation.async_get_conversation_languages(
hass, conversation.HOME_ASSISTANT_AGENT
),
country=hass.config.country,
)
if conversation_languages:
pipeline_language = hass.config.language
conversation_language = conversation_languages[0]
if stt_engine_id is None:
stt_engine_id = stt.async_default_engine(hass)
if stt_engine_id is not None:
stt_engine = stt.async_get_speech_to_text_engine(hass, stt_engine_id)
if stt_engine is None:
stt_engine_id = None
if stt_engine:
stt_languages = language_util.matches(
pipeline_language,
stt_engine.supported_languages,
country=hass.config.country,
)
if stt_languages:
stt_language = stt_languages[0]
else:
_LOGGER.debug(
"Speech-to-text engine '%s' does not support language '%s'",
stt_engine_id,
pipeline_language,
)
stt_engine_id = None
if tts_engine_id is None:
tts_engine_id = tts.async_default_engine(hass)
if tts_engine_id is not None:
tts_engine = tts.get_engine_instance(hass, tts_engine_id)
if tts_engine is None:
tts_engine_id = None
if tts_engine:
tts_languages = language_util.matches(
pipeline_language,
tts_engine.supported_languages,
country=hass.config.country,
)
if tts_languages:
tts_language = tts_languages[0]
tts_voices = tts_engine.async_get_supported_voices(tts_language)
if tts_voices:
tts_voice = tts_voices[0].voice_id
else:
_LOGGER.debug(
"Text-to-speech engine '%s' does not support language '%s'",
tts_engine_id,
pipeline_language,
)
tts_engine_id = None
return {
"conversation_engine": conversation.HOME_ASSISTANT_AGENT,
"conversation_language": conversation_language,
"language": hass.config.language,
"name": pipeline_name,
"stt_engine": stt_engine_id,
"stt_language": stt_language,
"tts_engine": tts_engine_id,
"tts_language": tts_language,
"tts_voice": tts_voice,
"wake_word_entity": wake_word_entity,
"wake_word_id": wake_word_id,
}
async def _async_create_default_pipeline(
hass: HomeAssistant, pipeline_store: PipelineStorageCollection
) -> Pipeline:
"""Create a default pipeline.
The default pipeline will use the homeassistant conversation agent and the
default stt / tts engines.
"""
pipeline_settings = await _async_resolve_default_pipeline_settings(
hass, stt_engine_id=None, tts_engine_id=None, pipeline_name="Home Assistant"
)
return await pipeline_store.async_create_item(pipeline_settings)
async def async_create_default_pipeline(
hass: HomeAssistant,
stt_engine_id: str,
tts_engine_id: str,
pipeline_name: str,
) -> Pipeline | None:
"""Create a pipeline with default settings.
The default pipeline will use the homeassistant conversation agent and the
specified stt / tts engines.
"""
pipeline_data: PipelineData = hass.data[DOMAIN]
pipeline_store = pipeline_data.pipeline_store
pipeline_settings = await _async_resolve_default_pipeline_settings(
hass, stt_engine_id, tts_engine_id, pipeline_name=pipeline_name
)
if (
pipeline_settings["stt_engine"] != stt_engine_id
or pipeline_settings["tts_engine"] != tts_engine_id
):
return None
return await pipeline_store.async_create_item(pipeline_settings)
@callback
def async_get_pipeline(hass: HomeAssistant, pipeline_id: str | None = None) -> Pipeline:
"""Get a pipeline by id or the preferred pipeline."""
pipeline_data: PipelineData = hass.data[DOMAIN]
if pipeline_id is None:
# A pipeline was not specified, use the preferred one
pipeline_id = pipeline_data.pipeline_store.async_get_preferred_item()
pipeline = pipeline_data.pipeline_store.data.get(pipeline_id)
# If invalid pipeline ID was specified
if pipeline is None:
raise PipelineNotFound(
"pipeline_not_found", f"Pipeline {pipeline_id} not found"
)
return pipeline
@callback
def async_get_pipelines(hass: HomeAssistant) -> Iterable[Pipeline]:
"""Get all pipelines."""
pipeline_data: PipelineData = hass.data[DOMAIN]
return pipeline_data.pipeline_store.data.values()
async def async_update_pipeline(
hass: HomeAssistant,
pipeline: Pipeline,
*,
conversation_engine: str | UndefinedType = UNDEFINED,
conversation_language: str | UndefinedType = UNDEFINED,
language: str | UndefinedType = UNDEFINED,
name: str | UndefinedType = UNDEFINED,
stt_engine: str | None | UndefinedType = UNDEFINED,
stt_language: str | None | UndefinedType = UNDEFINED,
tts_engine: str | None | UndefinedType = UNDEFINED,
tts_language: str | None | UndefinedType = UNDEFINED,
tts_voice: str | None | UndefinedType = UNDEFINED,
wake_word_entity: str | None | UndefinedType = UNDEFINED,
wake_word_id: str | None | UndefinedType = UNDEFINED,
) -> None:
"""Update a pipeline."""
pipeline_data: PipelineData = hass.data[DOMAIN]
updates: dict[str, Any] = pipeline.to_json()
updates.pop("id")
# Refactor this once we bump to Python 3.12
# and have https://peps.python.org/pep-0692/
for key, val in (
("conversation_engine", conversation_engine),
("conversation_language", conversation_language),
("language", language),
("name", name),
("stt_engine", stt_engine),
("stt_language", stt_language),
("tts_engine", tts_engine),
("tts_language", tts_language),
("tts_voice", tts_voice),
("wake_word_entity", wake_word_entity),
("wake_word_id", wake_word_id),
):
if val is not UNDEFINED:
updates[key] = val
await pipeline_data.pipeline_store.async_update_item(pipeline.id, updates)
class PipelineEventType(StrEnum):
"""Event types emitted during a pipeline run."""
RUN_START = "run-start"
RUN_END = "run-end"
WAKE_WORD_START = "wake_word-start"
WAKE_WORD_END = "wake_word-end"
STT_START = "stt-start"
STT_VAD_START = "stt-vad-start"
STT_VAD_END = "stt-vad-end"
STT_END = "stt-end"
INTENT_START = "intent-start"
INTENT_END = "intent-end"
TTS_START = "tts-start"
TTS_END = "tts-end"
ERROR = "error"
@dataclass(frozen=True)
class PipelineEvent:
"""Events emitted during a pipeline run."""
type: PipelineEventType
data: dict[str, Any] | None = None
timestamp: str = field(default_factory=lambda: dt_util.utcnow().isoformat())
PipelineEventCallback = Callable[[PipelineEvent], None]
@dataclass(frozen=True)
class Pipeline:
"""A voice assistant pipeline."""
conversation_engine: str
conversation_language: str
language: str
name: str
stt_engine: str | None
stt_language: str | None
tts_engine: str | None
tts_language: str | None
tts_voice: str | None
wake_word_entity: str | None
wake_word_id: str | None
id: str = field(default_factory=ulid_util.ulid_now)
@classmethod
def from_json(cls, data: dict[str, Any]) -> Pipeline:
"""Create an instance from a JSON serialization.
This function was added in HA Core 2023.10, previous versions will raise
if there are unexpected items in the serialized data.
"""
return cls(
conversation_engine=data["conversation_engine"],
conversation_language=data["conversation_language"],
id=data["id"],
language=data["language"],
name=data["name"],
stt_engine=data["stt_engine"],
stt_language=data["stt_language"],
tts_engine=data["tts_engine"],
tts_language=data["tts_language"],
tts_voice=data["tts_voice"],
wake_word_entity=data["wake_word_entity"],
wake_word_id=data["wake_word_id"],
)
def to_json(self) -> dict[str, Any]:
"""Return a JSON serializable representation for storage."""
return {
"conversation_engine": self.conversation_engine,
"conversation_language": self.conversation_language,
"id": self.id,
"language": self.language,
"name": self.name,
"stt_engine": self.stt_engine,
"stt_language": self.stt_language,
"tts_engine": self.tts_engine,
"tts_language": self.tts_language,
"tts_voice": self.tts_voice,
"wake_word_entity": self.wake_word_entity,
"wake_word_id": self.wake_word_id,
}
class PipelineStage(StrEnum):
"""Stages of a pipeline."""
WAKE_WORD = "wake_word"
STT = "stt"
INTENT = "intent"
TTS = "tts"
END = "end"
PIPELINE_STAGE_ORDER = [
PipelineStage.WAKE_WORD,
PipelineStage.STT,
PipelineStage.INTENT,
PipelineStage.TTS,
]
class PipelineRunValidationError(Exception):
"""Error when a pipeline run is not valid."""
class InvalidPipelineStagesError(PipelineRunValidationError):
"""Error when given an invalid combination of start/end stages."""
def __init__(
self,
start_stage: PipelineStage,
end_stage: PipelineStage,
) -> None:
"""Set error message."""
super().__init__(
f"Invalid stage combination: start={start_stage}, end={end_stage}"
)
@dataclass(frozen=True)
class WakeWordSettings:
"""Settings for wake word detection."""
timeout: float | None = None
"""Seconds of silence before detection times out."""
audio_seconds_to_buffer: float = 0
"""Seconds of audio to buffer before detection and forward to STT."""
cooldown_seconds: float = DEFAULT_WAKE_WORD_COOLDOWN
"""Seconds after a wake word detection where other detections are ignored."""
@dataclass(frozen=True)
class AudioSettings:
"""Settings for pipeline audio processing."""
noise_suppression_level: int = 0
"""Level of noise suppression (0 = disabled, 4 = max)"""
auto_gain_dbfs: int = 0
"""Amount of automatic gain in dbFS (0 = disabled, 31 = max)"""
volume_multiplier: float = 1.0
"""Multiplier used directly on PCM samples (1.0 = no change, 2.0 = twice as loud)"""
is_vad_enabled: bool = True
"""True if VAD is used to determine the end of the voice command."""
is_chunking_enabled: bool = True
"""True if audio is automatically split into 10 ms chunks (required for VAD, etc.)"""
def __post_init__(self) -> None:
"""Verify settings post-initialization."""
if (self.noise_suppression_level < 0) or (self.noise_suppression_level > 4):
raise ValueError("noise_suppression_level must be in [0, 4]")
if (self.auto_gain_dbfs < 0) or (self.auto_gain_dbfs > 31):
raise ValueError("auto_gain_dbfs must be in [0, 31]")
if self.needs_processor and (not self.is_chunking_enabled):
raise ValueError("Chunking must be enabled for audio processing")
@property
def needs_processor(self) -> bool:
"""True if an audio processor is needed."""
return (
self.is_vad_enabled
or (self.noise_suppression_level > 0)
or (self.auto_gain_dbfs > 0)
)
@dataclass(frozen=True, slots=True)
class ProcessedAudioChunk:
"""Processed audio chunk and metadata."""
audio: bytes
"""Raw PCM audio @ 16Khz with 16-bit mono samples"""
timestamp_ms: int
"""Timestamp relative to start of audio stream (milliseconds)"""
is_speech: bool | None
"""True if audio chunk likely contains speech, False if not, None if unknown"""
@dataclass
class PipelineRun:
"""Running context for a pipeline."""
hass: HomeAssistant
context: Context
pipeline: Pipeline
start_stage: PipelineStage
end_stage: PipelineStage
event_callback: PipelineEventCallback
language: str = None # type: ignore[assignment]
runner_data: Any | None = None
intent_agent: str | None = None
tts_audio_output: str | None = None
wake_word_settings: WakeWordSettings | None = None
audio_settings: AudioSettings = field(default_factory=AudioSettings)
id: str = field(default_factory=ulid_util.ulid_now)
stt_provider: stt.SpeechToTextEntity | stt.Provider = field(init=False, repr=False)
tts_engine: str = field(init=False, repr=False)
tts_options: dict | None = field(init=False, default=None)
wake_word_entity_id: str | None = field(init=False, default=None, repr=False)
wake_word_entity: wake_word.WakeWordDetectionEntity = field(init=False, repr=False)
abort_wake_word_detection: bool = field(init=False, default=False)
debug_recording_thread: Thread | None = None
"""Thread that records audio to debug_recording_dir"""
debug_recording_queue: Queue[str | bytes | None] | None = None
"""Queue to communicate with debug recording thread"""
audio_processor: AudioProcessor | None = None
"""VAD/noise suppression/auto gain"""
audio_processor_buffer: AudioBuffer = field(init=False, repr=False)
"""Buffer used when splitting audio into chunks for audio processing"""
_device_id: str | None = None
"""Optional device id set during run start."""
def __post_init__(self) -> None:
"""Set language for pipeline."""
self.language = self.pipeline.language or self.hass.config.language
# wake -> stt -> intent -> tts
if PIPELINE_STAGE_ORDER.index(self.end_stage) < PIPELINE_STAGE_ORDER.index(
self.start_stage
):
raise InvalidPipelineStagesError(self.start_stage, self.end_stage)
pipeline_data: PipelineData = self.hass.data[DOMAIN]
if self.pipeline.id not in pipeline_data.pipeline_debug:
pipeline_data.pipeline_debug[self.pipeline.id] = LimitedSizeDict(
size_limit=STORED_PIPELINE_RUNS
)
pipeline_data.pipeline_debug[self.pipeline.id][self.id] = PipelineRunDebug()
pipeline_data.pipeline_runs.add_run(self)
# Initialize with audio settings
self.audio_processor_buffer = AudioBuffer(AUDIO_PROCESSOR_BYTES)
if self.audio_settings.needs_processor:
# Delay import of webrtc so HA start up is not crashing
# on older architectures (armhf).
#
# pylint: disable=import-outside-toplevel
from webrtc_noise_gain import AudioProcessor
self.audio_processor = AudioProcessor(
self.audio_settings.auto_gain_dbfs,
self.audio_settings.noise_suppression_level,
)
def __eq__(self, other: Any) -> bool:
"""Compare pipeline runs by id."""
if isinstance(other, PipelineRun):
return self.id == other.id
return False
@callback
def process_event(self, event: PipelineEvent) -> None:
"""Log an event and call listener."""
self.event_callback(event)
pipeline_data: PipelineData = self.hass.data[DOMAIN]
if self.id not in pipeline_data.pipeline_debug[self.pipeline.id]:
# This run has been evicted from the logged pipeline runs already
return
pipeline_data.pipeline_debug[self.pipeline.id][self.id].events.append(event)
def start(self, device_id: str | None) -> None:
"""Emit run start event."""
self._device_id = device_id
self._start_debug_recording_thread()
data = {
"pipeline": self.pipeline.id,
"language": self.language,
}
if self.runner_data is not None:
data["runner_data"] = self.runner_data
self.process_event(PipelineEvent(PipelineEventType.RUN_START, data))
async def end(self) -> None:
"""Emit run end event."""
# Signal end of stream to listeners
self._capture_chunk(None)
# Stop the recording thread before emitting run-end.
# This ensures that files are properly closed if the event handler reads them.
await self._stop_debug_recording_thread()
self.process_event(
PipelineEvent(
PipelineEventType.RUN_END,
)
)
pipeline_data: PipelineData = self.hass.data[DOMAIN]
pipeline_data.pipeline_runs.remove_run(self)
async def prepare_wake_word_detection(self) -> None:
"""Prepare wake-word-detection."""
entity_id = self.pipeline.wake_word_entity or wake_word.async_default_entity(
self.hass
)
if entity_id is None:
raise WakeWordDetectionError(
code="wake-engine-missing",
message="No wake word engine",
)
wake_word_entity = wake_word.async_get_wake_word_detection_entity(
self.hass, entity_id
)
if wake_word_entity is None:
raise WakeWordDetectionError(
code="wake-provider-missing",
message=f"No wake-word-detection provider for: {entity_id}",
)
self.wake_word_entity_id = entity_id
self.wake_word_entity = wake_word_entity
async def wake_word_detection(
self,
stream: AsyncIterable[ProcessedAudioChunk],
audio_chunks_for_stt: list[ProcessedAudioChunk],
) -> wake_word.DetectionResult | None:
"""Run wake-word-detection portion of pipeline. Returns detection result."""
metadata_dict = asdict(
stt.SpeechMetadata(
language="",
format=stt.AudioFormats.WAV,
codec=stt.AudioCodecs.PCM,
bit_rate=stt.AudioBitRates.BITRATE_16,
sample_rate=stt.AudioSampleRates.SAMPLERATE_16000,
channel=stt.AudioChannels.CHANNEL_MONO,
)
)
wake_word_settings = self.wake_word_settings or WakeWordSettings()
# Remove language since it doesn't apply to wake words yet
metadata_dict.pop("language", None)
self.process_event(
PipelineEvent(
PipelineEventType.WAKE_WORD_START,
{
"entity_id": self.wake_word_entity_id,
"metadata": metadata_dict,
"timeout": wake_word_settings.timeout or 0,
},
)
)
if self.debug_recording_queue is not None:
self.debug_recording_queue.put_nowait(f"00_wake-{self.wake_word_entity_id}")
wake_word_vad: VoiceActivityTimeout | None = None
if (wake_word_settings.timeout is not None) and (
wake_word_settings.timeout > 0
):
# Use VAD to determine timeout
wake_word_vad = VoiceActivityTimeout(wake_word_settings.timeout)
# Audio chunk buffer. This audio will be forwarded to speech-to-text
# after wake-word-detection.
num_audio_chunks_to_buffer = int(
(wake_word_settings.audio_seconds_to_buffer * 16000)
/ AUDIO_PROCESSOR_SAMPLES
)
stt_audio_buffer: deque[ProcessedAudioChunk] | None = None
if num_audio_chunks_to_buffer > 0:
stt_audio_buffer = deque(maxlen=num_audio_chunks_to_buffer)
try:
# Detect wake word(s)
result = await self.wake_word_entity.async_process_audio_stream(
self._wake_word_audio_stream(
audio_stream=stream,
stt_audio_buffer=stt_audio_buffer,
wake_word_vad=wake_word_vad,
),
self.pipeline.wake_word_id,
)
if stt_audio_buffer is not None:
# All audio kept from right before the wake word was detected as
# a single chunk.
audio_chunks_for_stt.extend(stt_audio_buffer)
except WakeWordDetectionAborted:
raise
except WakeWordTimeoutError:
_LOGGER.debug("Timeout during wake word detection")
raise
except Exception as src_error:
_LOGGER.exception("Unexpected error during wake-word-detection")
raise WakeWordDetectionError(
code="wake-stream-failed",
message="Unexpected error during wake-word-detection",
) from src_error
_LOGGER.debug("wake-word-detection result %s", result)
if result is None:
wake_word_output: dict[str, Any] = {}
else:
# Avoid duplicate detections by checking cooldown
wake_up_key = f"{self.wake_word_entity_id}.{result.wake_word_id}"
last_wake_up = self.hass.data[DATA_LAST_WAKE_UP].get(wake_up_key)
if last_wake_up is not None:
sec_since_last_wake_up = time.monotonic() - last_wake_up
if sec_since_last_wake_up < wake_word_settings.cooldown_seconds:
_LOGGER.debug("Duplicate wake word detection occurred")
raise WakeWordDetectionAborted
# Record last wake up time to block duplicate detections
self.hass.data[DATA_LAST_WAKE_UP][wake_up_key] = time.monotonic()
if result.queued_audio:
# Add audio that was pending at detection.
#
# Because detection occurs *after* the wake word was actually
# spoken, we need to make sure pending audio is forwarded to
# speech-to-text so the user does not have to pause before
# speaking the voice command.
for chunk_ts in result.queued_audio:
audio_chunks_for_stt.append(
ProcessedAudioChunk(
audio=chunk_ts[0], timestamp_ms=chunk_ts[1], is_speech=False
)
)
wake_word_output = asdict(result)
# Remove non-JSON fields
wake_word_output.pop("queued_audio", None)
self.process_event(
PipelineEvent(
PipelineEventType.WAKE_WORD_END,
{"wake_word_output": wake_word_output},
)
)
return result
async def _wake_word_audio_stream(
self,
audio_stream: AsyncIterable[ProcessedAudioChunk],
stt_audio_buffer: deque[ProcessedAudioChunk] | None,
wake_word_vad: VoiceActivityTimeout | None,
sample_rate: int = 16000,
sample_width: int = 2,
) -> AsyncIterable[tuple[bytes, int]]:
"""Yield audio chunks with timestamps (milliseconds since start of stream).
Adds audio to a ring buffer that will be forwarded to speech-to-text after
detection. Times out if VAD detects enough silence.
"""
chunk_seconds = AUDIO_PROCESSOR_SAMPLES / sample_rate
async for chunk in audio_stream:
if self.abort_wake_word_detection:
raise WakeWordDetectionAborted
self._capture_chunk(chunk.audio)
yield chunk.audio, chunk.timestamp_ms
# Wake-word-detection occurs *after* the wake word was actually
# spoken. Keeping audio right before detection allows the voice
# command to be spoken immediately after the wake word.
if stt_audio_buffer is not None:
stt_audio_buffer.append(chunk)
if wake_word_vad is not None:
if not wake_word_vad.process(chunk_seconds, chunk.is_speech):
raise WakeWordTimeoutError(
code="wake-word-timeout", message="Wake word was not detected"
)
async def prepare_speech_to_text(self, metadata: stt.SpeechMetadata) -> None:
"""Prepare speech-to-text."""
# pipeline.stt_engine can't be None or this function is not called
stt_provider = stt.async_get_speech_to_text_engine(
self.hass,
self.pipeline.stt_engine, # type: ignore[arg-type]
)
if stt_provider is None:
engine = self.pipeline.stt_engine
raise SpeechToTextError(
code="stt-provider-missing",
message=f"No speech-to-text provider for: {engine}",
)
metadata.language = self.pipeline.stt_language or self.language
if not stt_provider.check_metadata(metadata):
raise SpeechToTextError(
code="stt-provider-unsupported-metadata",
message=(
f"Provider {stt_provider.name} does not support input speech "
f"to text metadata {metadata}"
),
)
self.stt_provider = stt_provider
async def speech_to_text(
self,
metadata: stt.SpeechMetadata,
stream: AsyncIterable[ProcessedAudioChunk],
) -> str:
"""Run speech-to-text portion of pipeline. Returns the spoken text."""
if isinstance(self.stt_provider, stt.Provider):
engine = self.stt_provider.name
else:
engine = self.stt_provider.entity_id
self.process_event(
PipelineEvent(
PipelineEventType.STT_START,
{
"engine": engine,
"metadata": asdict(metadata),
},
)
)
if self.debug_recording_queue is not None:
# New recording
self.debug_recording_queue.put_nowait(f"01_stt-{engine}")
try:
# Transcribe audio stream
stt_vad: VoiceCommandSegmenter | None = None
if self.audio_settings.is_vad_enabled:
stt_vad = VoiceCommandSegmenter()
result = await self.stt_provider.async_process_audio_stream(
metadata,
self._speech_to_text_stream(audio_stream=stream, stt_vad=stt_vad),
)
except Exception as src_error:
_LOGGER.exception("Unexpected error during speech-to-text")
raise SpeechToTextError(
code="stt-stream-failed",
message="Unexpected error during speech-to-text",
) from src_error
_LOGGER.debug("speech-to-text result %s", result)
if result.result != stt.SpeechResultState.SUCCESS:
raise SpeechToTextError(
code="stt-stream-failed",
message="speech-to-text failed",
)
if not result.text:
raise SpeechToTextError(
code="stt-no-text-recognized", message="No text recognized"
)
self.process_event(
PipelineEvent(
PipelineEventType.STT_END,
{
"stt_output": {
"text": result.text,
}
},
)
)
return result.text
async def _speech_to_text_stream(
self,
audio_stream: AsyncIterable[ProcessedAudioChunk],
stt_vad: VoiceCommandSegmenter | None,
sample_rate: int = 16000,
sample_width: int = 2,
) -> AsyncGenerator[bytes, None]:
"""Yield audio chunks until VAD detects silence or speech-to-text completes."""
chunk_seconds = AUDIO_PROCESSOR_SAMPLES / sample_rate
sent_vad_start = False
async for chunk in audio_stream:
self._capture_chunk(chunk.audio)
if stt_vad is not None:
if not stt_vad.process(chunk_seconds, chunk.is_speech):
# Silence detected at the end of voice command
self.process_event(
PipelineEvent(
PipelineEventType.STT_VAD_END,
{"timestamp": chunk.timestamp_ms},
)
)
break
if stt_vad.in_command and (not sent_vad_start):
# Speech detected at start of voice command
self.process_event(
PipelineEvent(
PipelineEventType.STT_VAD_START,
{"timestamp": chunk.timestamp_ms},
)
)
sent_vad_start = True
yield chunk.audio
async def prepare_recognize_intent(self) -> None:
"""Prepare recognizing an intent."""
agent_info = conversation.async_get_agent_info(
self.hass,
# If no conversation engine is set, use the Home Assistant agent
# (the conversation integration default is currently the last one set)
self.pipeline.conversation_engine or conversation.HOME_ASSISTANT_AGENT,
)
if agent_info is None:
engine = self.pipeline.conversation_engine or "default"
raise IntentRecognitionError(
code="intent-not-supported",
message=f"Intent recognition engine {engine} is not found",
)
self.intent_agent = agent_info.id
async def recognize_intent(
self, intent_input: str, conversation_id: str | None, device_id: str | None
) -> str:
"""Run intent recognition portion of pipeline. Returns text to speak."""
if self.intent_agent is None:
raise RuntimeError("Recognize intent was not prepared")
self.process_event(
PipelineEvent(
PipelineEventType.INTENT_START,
{
"engine": self.intent_agent,
"language": self.pipeline.conversation_language,
"intent_input": intent_input,
"conversation_id": conversation_id,
"device_id": device_id,
},
)
)
try:
conversation_result = await conversation.async_converse(
hass=self.hass,
text=intent_input,
conversation_id=conversation_id,
device_id=device_id,
context=self.context,
language=self.pipeline.conversation_language,
agent_id=self.intent_agent,
)
except Exception as src_error:
_LOGGER.exception("Unexpected error during intent recognition")
raise IntentRecognitionError(
code="intent-failed",
message="Unexpected error during intent recognition",
) from src_error
_LOGGER.debug("conversation result %s", conversation_result)
self.process_event(
PipelineEvent(
PipelineEventType.INTENT_END,
{"intent_output": conversation_result.as_dict()},
)
)
speech: str = conversation_result.response.speech.get("plain", {}).get(
"speech", ""
)
return speech
async def prepare_text_to_speech(self) -> None:
"""Prepare text-to-speech."""
# pipeline.tts_engine can't be None or this function is not called
engine = cast(str, self.pipeline.tts_engine)
tts_options: dict[str, Any] = {}
if self.pipeline.tts_voice is not None:
tts_options[tts.ATTR_VOICE] = self.pipeline.tts_voice
if self.tts_audio_output is not None:
tts_options[tts.ATTR_PREFERRED_FORMAT] = self.tts_audio_output
if self.tts_audio_output == "wav":
# 16 Khz, 16-bit mono
tts_options[tts.ATTR_PREFERRED_SAMPLE_RATE] = 16000
tts_options[tts.ATTR_PREFERRED_SAMPLE_CHANNELS] = 1
try:
options_supported = await tts.async_support_options(
self.hass,
engine,
self.pipeline.tts_language,
tts_options,
)
except HomeAssistantError as err:
raise TextToSpeechError(
code="tts-not-supported",
message=f"Text-to-speech engine '{engine}' not found",
) from err
if not options_supported:
raise TextToSpeechError(
code="tts-not-supported",
message=(
f"Text-to-speech engine {engine} "
f"does not support language {self.pipeline.tts_language} or options {tts_options}"
),
)
self.tts_engine = engine
self.tts_options = tts_options
async def text_to_speech(self, tts_input: str) -> None:
"""Run text-to-speech portion of pipeline."""
self.process_event(
PipelineEvent(
PipelineEventType.TTS_START,
{
"engine": self.tts_engine,
"language": self.pipeline.tts_language,
"voice": self.pipeline.tts_voice,
"tts_input": tts_input,
},
)
)
try:
# Synthesize audio and get URL
tts_media_id = tts_generate_media_source_id(
self.hass,
tts_input,
engine=self.tts_engine,
language=self.pipeline.tts_language,
options=self.tts_options,
)
tts_media = await media_source.async_resolve_media(
self.hass,
tts_media_id,
None,
)
except Exception as src_error:
_LOGGER.exception("Unexpected error during text-to-speech")
raise TextToSpeechError(
code="tts-failed",
message="Unexpected error during text-to-speech",
) from src_error
_LOGGER.debug("TTS result %s", tts_media)
tts_output = {
"media_id": tts_media_id,
**asdict(tts_media),
}
self.process_event(
PipelineEvent(PipelineEventType.TTS_END, {"tts_output": tts_output})
)
def _capture_chunk(self, audio_bytes: bytes | None) -> None:
"""Forward audio chunk to various capturing mechanisms."""
if self.debug_recording_queue is not None:
# Forward to debug WAV file recording
self.debug_recording_queue.put_nowait(audio_bytes)
if self._device_id is None:
return
# Forward to device audio capture
pipeline_data: PipelineData = self.hass.data[DOMAIN]
audio_queue = pipeline_data.device_audio_queues.get(self._device_id)
if audio_queue is None:
return
try:
audio_queue.queue.put_nowait(audio_bytes)
except asyncio.QueueFull:
audio_queue.overflow = True
_LOGGER.warning("Audio queue full for device %s", self._device_id)
def _start_debug_recording_thread(self) -> None:
"""Start thread to record wake/stt audio if debug_recording_dir is set."""
if self.debug_recording_thread is not None:
# Already started
return
# Directory to save audio for each pipeline run.
# Configured in YAML for assist_pipeline.
if debug_recording_dir := self.hass.data[DATA_CONFIG].get(
CONF_DEBUG_RECORDING_DIR
):
if self._device_id is None:
# <debug_recording_dir>/<pipeline.name>/<run.id>
run_recording_dir = (
Path(debug_recording_dir)
/ self.pipeline.name
/ str(time.monotonic_ns())
)
else:
# <debug_recording_dir>/<device_id>/<pipeline.name>/<run.id>
run_recording_dir = (
Path(debug_recording_dir)
/ self._device_id
/ self.pipeline.name
/ str(time.monotonic_ns())
)
self.debug_recording_queue = Queue()
self.debug_recording_thread = Thread(
target=_pipeline_debug_recording_thread_proc,
args=(run_recording_dir, self.debug_recording_queue),
daemon=True,
)
self.debug_recording_thread.start()
async def _stop_debug_recording_thread(self) -> None:
"""Stop recording thread."""
if (self.debug_recording_thread is None) or (
self.debug_recording_queue is None
):
# Not running
return
# NOTE: Expecting a None to have been put in self.debug_recording_queue
# in self.end() to signal the thread to stop.
# Wait until the thread has finished to ensure that files are fully written
await self.hass.async_add_executor_job(self.debug_recording_thread.join)
self.debug_recording_queue = None
self.debug_recording_thread = None
async def process_volume_only(
self,
audio_stream: AsyncIterable[bytes],
sample_rate: int = 16000,
sample_width: int = 2,
) -> AsyncGenerator[ProcessedAudioChunk, None]:
"""Apply volume transformation only (no VAD/audio enhancements) with optional chunking."""
ms_per_sample = sample_rate // 1000
ms_per_chunk = (AUDIO_PROCESSOR_SAMPLES // sample_width) // ms_per_sample
timestamp_ms = 0
async for chunk in audio_stream:
if self.audio_settings.volume_multiplier != 1.0:
chunk = _multiply_volume(chunk, self.audio_settings.volume_multiplier)
if self.audio_settings.is_chunking_enabled:
# 10 ms chunking
for chunk_10ms in chunk_samples(
chunk, AUDIO_PROCESSOR_BYTES, self.audio_processor_buffer
):
yield ProcessedAudioChunk(
audio=chunk_10ms,
timestamp_ms=timestamp_ms,
is_speech=None, # no VAD
)
timestamp_ms += ms_per_chunk
else:
# No chunking
yield ProcessedAudioChunk(
audio=chunk,
timestamp_ms=timestamp_ms,
is_speech=None, # no VAD
)
timestamp_ms += (len(chunk) // sample_width) // ms_per_sample
async def process_enhance_audio(
self,
audio_stream: AsyncIterable[bytes],
sample_rate: int = 16000,
sample_width: int = 2,
) -> AsyncGenerator[ProcessedAudioChunk, None]:
"""Split audio into 10 ms chunks and apply VAD/noise suppression/auto gain/volume transformation."""
assert self.audio_processor is not None
ms_per_sample = sample_rate // 1000
ms_per_chunk = (AUDIO_PROCESSOR_SAMPLES // sample_width) // ms_per_sample
timestamp_ms = 0
async for dirty_samples in audio_stream:
if self.audio_settings.volume_multiplier != 1.0:
# Static gain
dirty_samples = _multiply_volume(
dirty_samples, self.audio_settings.volume_multiplier
)
# Split into 10ms chunks for audio enhancements/VAD
for dirty_10ms_chunk in chunk_samples(
dirty_samples, AUDIO_PROCESSOR_BYTES, self.audio_processor_buffer
):
ap_result = self.audio_processor.Process10ms(dirty_10ms_chunk)
yield ProcessedAudioChunk(
audio=ap_result.audio,
timestamp_ms=timestamp_ms,
is_speech=ap_result.is_speech,
)
timestamp_ms += ms_per_chunk
def _multiply_volume(chunk: bytes, volume_multiplier: float) -> bytes:
"""Multiplies 16-bit PCM samples by a constant."""
def _clamp(val: float) -> float:
"""Clamp to signed 16-bit."""
return max(-32768, min(32767, val))
return array.array(
"h",
(int(_clamp(value * volume_multiplier)) for value in array.array("h", chunk)),
).tobytes()
def _pipeline_debug_recording_thread_proc(
run_recording_dir: Path,
queue: Queue[str | bytes | None],
message_timeout: float = 5,
) -> None:
wav_writer: wave.Wave_write | None = None
try:
_LOGGER.debug("Saving wake/stt audio to %s", run_recording_dir)
run_recording_dir.mkdir(parents=True, exist_ok=True)
while True:
message = queue.get(timeout=message_timeout)
if message is None:
# Stop signal
break
if isinstance(message, str):
# New WAV file name
if wav_writer is not None:
wav_writer.close()
wav_path = run_recording_dir / f"{message}.wav"
wav_writer = wave.open(str(wav_path), "wb")
wav_writer.setframerate(16000)
wav_writer.setsampwidth(2)
wav_writer.setnchannels(1)
elif isinstance(message, bytes):
# Chunk of 16-bit mono audio at 16Khz
if wav_writer is not None:
wav_writer.writeframes(message)
except Empty:
pass # occurs when pipeline has unexpected error
except Exception: # pylint: disable=broad-exception-caught
_LOGGER.exception("Unexpected error in debug recording thread")
finally:
if wav_writer is not None:
wav_writer.close()
@dataclass
class PipelineInput:
"""Input to a pipeline run."""
run: PipelineRun
stt_metadata: stt.SpeechMetadata | None = None
"""Metadata of stt input audio. Required when start_stage = stt."""
stt_stream: AsyncIterable[bytes] | None = None
"""Input audio for stt. Required when start_stage = stt."""
intent_input: str | None = None
"""Input for conversation agent. Required when start_stage = intent."""
tts_input: str | None = None
"""Input for text-to-speech. Required when start_stage = tts."""
conversation_id: str | None = None
device_id: str | None = None
async def execute(self) -> None:
"""Run pipeline."""
self.run.start(device_id=self.device_id)
current_stage: PipelineStage | None = self.run.start_stage
stt_audio_buffer: list[ProcessedAudioChunk] = []
stt_processed_stream: AsyncIterable[ProcessedAudioChunk] | None = None
if self.stt_stream is not None:
if self.run.audio_settings.needs_processor:
# VAD/noise suppression/auto gain/volume
stt_processed_stream = self.run.process_enhance_audio(self.stt_stream)
else:
# Volume multiplier only
stt_processed_stream = self.run.process_volume_only(self.stt_stream)
try:
if current_stage == PipelineStage.WAKE_WORD:
# wake-word-detection
assert stt_processed_stream is not None
detect_result = await self.run.wake_word_detection(
stt_processed_stream, stt_audio_buffer
)
if detect_result is None:
# No wake word. Abort the rest of the pipeline.
return
current_stage = PipelineStage.STT
# speech-to-text
intent_input = self.intent_input
if current_stage == PipelineStage.STT:
assert self.stt_metadata is not None
assert stt_processed_stream is not None
stt_input_stream = stt_processed_stream
if stt_audio_buffer:
# Send audio in the buffer first to speech-to-text, then move on to stt_stream.
# This is basically an async itertools.chain.
async def buffer_then_audio_stream() -> (
AsyncGenerator[ProcessedAudioChunk, None]
):
# Buffered audio
for chunk in stt_audio_buffer:
yield chunk
# Streamed audio
assert stt_processed_stream is not None
async for chunk in stt_processed_stream:
yield chunk
stt_input_stream = buffer_then_audio_stream()
intent_input = await self.run.speech_to_text(
self.stt_metadata,
stt_input_stream,
)
current_stage = PipelineStage.INTENT
if self.run.end_stage != PipelineStage.STT:
tts_input = self.tts_input
if current_stage == PipelineStage.INTENT:
# intent-recognition
assert intent_input is not None
tts_input = await self.run.recognize_intent(
intent_input,
self.conversation_id,
self.device_id,
)
if tts_input.strip():
current_stage = PipelineStage.TTS
else:
# Skip TTS
current_stage = PipelineStage.END
if self.run.end_stage != PipelineStage.INTENT:
# text-to-speech
if current_stage == PipelineStage.TTS:
assert tts_input is not None
await self.run.text_to_speech(tts_input)
except PipelineError as err:
self.run.process_event(
PipelineEvent(
PipelineEventType.ERROR,
{"code": err.code, "message": err.message},
)
)
finally:
# Always end the run since it needs to shut down the debug recording
# thread, etc.
await self.run.end()
async def validate(self) -> None:
"""Validate pipeline input against start stage."""
if self.run.start_stage in (PipelineStage.WAKE_WORD, PipelineStage.STT):
if self.run.pipeline.stt_engine is None:
raise PipelineRunValidationError(
"the pipeline does not support speech-to-text"
)
if self.stt_metadata is None:
raise PipelineRunValidationError(
"stt_metadata is required for speech-to-text"
)
if self.stt_stream is None:
raise PipelineRunValidationError(
"stt_stream is required for speech-to-text"
)
elif self.run.start_stage == PipelineStage.INTENT:
if self.intent_input is None:
raise PipelineRunValidationError(
"intent_input is required for intent recognition"
)
elif self.run.start_stage == PipelineStage.TTS:
if self.tts_input is None:
raise PipelineRunValidationError(
"tts_input is required for text-to-speech"
)
if self.run.end_stage == PipelineStage.TTS:
if self.run.pipeline.tts_engine is None:
raise PipelineRunValidationError(
"the pipeline does not support text-to-speech"
)
start_stage_index = PIPELINE_STAGE_ORDER.index(self.run.start_stage)
end_stage_index = PIPELINE_STAGE_ORDER.index(self.run.end_stage)
prepare_tasks = []
if (
start_stage_index
<= PIPELINE_STAGE_ORDER.index(PipelineStage.WAKE_WORD)
<= end_stage_index
):
prepare_tasks.append(self.run.prepare_wake_word_detection())
if (
start_stage_index
<= PIPELINE_STAGE_ORDER.index(PipelineStage.STT)
<= end_stage_index
):
# self.stt_metadata can't be None or we'd raise above
prepare_tasks.append(self.run.prepare_speech_to_text(self.stt_metadata)) # type: ignore[arg-type]
if (
start_stage_index
<= PIPELINE_STAGE_ORDER.index(PipelineStage.INTENT)
<= end_stage_index
):
prepare_tasks.append(self.run.prepare_recognize_intent())
if (
start_stage_index
<= PIPELINE_STAGE_ORDER.index(PipelineStage.TTS)
<= end_stage_index
):
prepare_tasks.append(self.run.prepare_text_to_speech())
if prepare_tasks:
await asyncio.gather(*prepare_tasks)
class PipelinePreferred(CollectionError):
"""Raised when attempting to delete the preferred pipelen."""
def __init__(self, item_id: str) -> None:
"""Initialize pipeline preferred error."""
super().__init__(f"Item {item_id} preferred.")
self.item_id = item_id
class SerializedPipelineStorageCollection(SerializedStorageCollection):
"""Serialized pipeline storage collection."""
preferred_item: str
class PipelineStorageCollection(
StorageCollection[Pipeline, SerializedPipelineStorageCollection]
):
"""Pipeline storage collection."""
_preferred_item: str
async def _async_load_data(self) -> SerializedPipelineStorageCollection | None:
"""Load the data."""
if not (data := await super()._async_load_data()):
pipeline = await _async_create_default_pipeline(self.hass, self)
self._preferred_item = pipeline.id
return data
self._preferred_item = data["preferred_item"]
return data
async def _process_create_data(self, data: dict) -> dict:
"""Validate the config is valid."""
validated_data: dict = validate_language(data)
return validated_data
@callback
def _get_suggested_id(self, info: dict) -> str:
"""Suggest an ID based on the config."""
return ulid_util.ulid_now()
async def _update_data(self, item: Pipeline, update_data: dict) -> Pipeline:
"""Return a new updated item."""
update_data = validate_language(update_data)
return Pipeline(id=item.id, **update_data)
def _create_item(self, item_id: str, data: dict) -> Pipeline:
"""Create an item from validated config."""
return Pipeline(id=item_id, **data)
def _deserialize_item(self, data: dict) -> Pipeline:
"""Create an item from its serialized representation."""
return Pipeline.from_json(data)
def _serialize_item(self, item_id: str, item: Pipeline) -> dict:
"""Return the serialized representation of an item for storing."""
return item.to_json()
async def async_delete_item(self, item_id: str) -> None:
"""Delete item."""
if self._preferred_item == item_id:
raise PipelinePreferred(item_id)
await super().async_delete_item(item_id)
@callback
def async_get_preferred_item(self) -> str:
"""Get the id of the preferred item."""
return self._preferred_item
@callback
def async_set_preferred_item(self, item_id: str) -> None:
"""Set the preferred pipeline."""
if item_id not in self.data:
raise ItemNotFound(item_id)
self._preferred_item = item_id
self._async_schedule_save()
@callback
def _data_to_save(self) -> SerializedPipelineStorageCollection:
"""Return JSON-compatible date for storing to file."""
base_data = super()._base_data_to_save()
return {
"items": base_data["items"],
"preferred_item": self._preferred_item,
}
class PipelineStorageCollectionWebsocket(
StorageCollectionWebsocket[PipelineStorageCollection]
):
"""Class to expose storage collection management over websocket."""
@callback
def async_setup(
self,
hass: HomeAssistant,
*,
create_list: bool = True,
create_create: bool = True,
) -> None:
"""Set up the websocket commands."""
super().async_setup(hass, create_list=create_list, create_create=create_create)
websocket_api.async_register_command(
hass,
f"{self.api_prefix}/get",
self.ws_get_item,
websocket_api.BASE_COMMAND_MESSAGE_SCHEMA.extend(
{
vol.Required("type"): f"{self.api_prefix}/get",
vol.Optional(self.item_id_key): str,
}
),
)
websocket_api.async_register_command(
hass,
f"{self.api_prefix}/set_preferred",
websocket_api.require_admin(
websocket_api.async_response(self.ws_set_preferred_item)
),
websocket_api.BASE_COMMAND_MESSAGE_SCHEMA.extend(
{
vol.Required("type"): f"{self.api_prefix}/set_preferred",
vol.Required(self.item_id_key): str,
}
),
)
async def ws_delete_item(
self, hass: HomeAssistant, connection: websocket_api.ActiveConnection, msg: dict
) -> None:
"""Delete an item."""
try:
await super().ws_delete_item(hass, connection, msg)
except PipelinePreferred as exc:
connection.send_error(
msg["id"], websocket_api.const.ERR_NOT_ALLOWED, str(exc)
)
@callback
def ws_get_item(
self, hass: HomeAssistant, connection: websocket_api.ActiveConnection, msg: dict
) -> None:
"""Get an item."""
item_id = msg.get(self.item_id_key)
if item_id is None:
item_id = self.storage_collection.async_get_preferred_item()
if item_id not in self.storage_collection.data:
connection.send_error(
msg["id"],
websocket_api.const.ERR_NOT_FOUND,
f"Unable to find {self.item_id_key} {item_id}",
)
return
connection.send_result(msg["id"], self.storage_collection.data[item_id])
@callback
def ws_list_item(
self, hass: HomeAssistant, connection: websocket_api.ActiveConnection, msg: dict
) -> None:
"""List items."""
connection.send_result(
msg["id"],
{
"pipelines": self.storage_collection.async_items(),
"preferred_pipeline": self.storage_collection.async_get_preferred_item(),
},
)
async def ws_set_preferred_item(
self,
hass: HomeAssistant,
connection: websocket_api.ActiveConnection,
msg: dict[str, Any],
) -> None:
"""Set the preferred item."""
try:
self.storage_collection.async_set_preferred_item(msg[self.item_id_key])
except ItemNotFound:
connection.send_error(
msg["id"], websocket_api.const.ERR_NOT_FOUND, "unknown item"
)
return
connection.send_result(msg["id"])
class PipelineRuns:
"""Class managing pipelineruns."""
def __init__(self, pipeline_store: PipelineStorageCollection) -> None:
"""Initialize."""
self._pipeline_runs: dict[str, dict[str, PipelineRun]] = defaultdict(dict)
self._pipeline_store = pipeline_store
pipeline_store.async_add_listener(self._change_listener)
def add_run(self, pipeline_run: PipelineRun) -> None:
"""Add pipeline run."""
pipeline_id = pipeline_run.pipeline.id
self._pipeline_runs[pipeline_id][pipeline_run.id] = pipeline_run
def remove_run(self, pipeline_run: PipelineRun) -> None:
"""Remove pipeline run."""
pipeline_id = pipeline_run.pipeline.id
self._pipeline_runs[pipeline_id].pop(pipeline_run.id)
async def _change_listener(
self, change_type: str, item_id: str, change: dict
) -> None:
"""Handle pipeline store changes."""
if change_type != CHANGE_UPDATED:
return
if pipeline_runs := self._pipeline_runs.get(item_id):
# Create a temporary list in case the list is modified while we iterate
for pipeline_run in list(pipeline_runs.values()):
pipeline_run.abort_wake_word_detection = True
@dataclass(slots=True)
class DeviceAudioQueue:
"""Audio capture queue for a satellite device."""
queue: asyncio.Queue[bytes | None]
"""Queue of audio chunks (None = stop signal)"""
id: str = field(default_factory=ulid_util.ulid_now)
"""Unique id to ensure the correct audio queue is cleaned up in websocket API."""
overflow: bool = False
"""Flag to be set if audio samples were dropped because the queue was full."""
@dataclass(slots=True)
class AssistDevice:
"""Assist device."""
domain: str
unique_id_prefix: str
class PipelineData:
"""Store and debug data stored in hass.data."""
def __init__(self, pipeline_store: PipelineStorageCollection) -> None:
"""Initialize."""
self.pipeline_store = pipeline_store
self.pipeline_debug: dict[str, LimitedSizeDict[str, PipelineRunDebug]] = {}
self.pipeline_devices: dict[str, AssistDevice] = {}
self.pipeline_runs = PipelineRuns(pipeline_store)
self.device_audio_queues: dict[str, DeviceAudioQueue] = {}
@dataclass(slots=True)
class PipelineRunDebug:
"""Debug data for a pipelinerun."""
events: list[PipelineEvent] = field(default_factory=list, init=False)
timestamp: str = field(
default_factory=lambda: dt_util.utcnow().isoformat(),
init=False,
)
class PipelineStore(Store[SerializedPipelineStorageCollection]):
"""Store entity registry data."""
async def _async_migrate_func(
self,
old_major_version: int,
old_minor_version: int,
old_data: SerializedPipelineStorageCollection,
) -> SerializedPipelineStorageCollection:
"""Migrate to the new version."""
if old_major_version == 1 and old_minor_version < 2:
# Version 1.2 adds wake word configuration
for pipeline in old_data["items"]:
# Populate keys which were introduced before version 1.2
pipeline.setdefault("wake_word_entity", None)
pipeline.setdefault("wake_word_id", None)
if old_major_version > 1:
raise NotImplementedError
return old_data
@singleton(DOMAIN)
async def async_setup_pipeline_store(hass: HomeAssistant) -> PipelineData:
"""Set up the pipeline storage collection."""
pipeline_store = PipelineStorageCollection(
PipelineStore(
hass, STORAGE_VERSION, STORAGE_KEY, minor_version=STORAGE_VERSION_MINOR
)
)
await pipeline_store.async_load()
PipelineStorageCollectionWebsocket(
pipeline_store,
f"{DOMAIN}/pipeline",
"pipeline",
PIPELINE_FIELDS,
PIPELINE_FIELDS,
).async_setup(hass)
return PipelineData(pipeline_store)