From 79ce3271942e0eb555e222f80e2dfb84a6216d32 Mon Sep 17 00:00:00 2001 From: Michael Hansen Date: Thu, 27 Jun 2024 16:47:25 -0500 Subject: [PATCH] Working --- requirements.txt | 2 +- tests/test_openwakeword.py | 2 +- wyoming_openwakeword/VERSION | 2 +- wyoming_openwakeword/__main__.py | 84 ++-- wyoming_openwakeword/handler.py | 312 +++++++------- wyoming_openwakeword/models/timer_v0.1.tflite | Bin 0 -> 1743316 bytes .../models/weather_v0.1.tflite | Bin 0 -> 1150224 bytes wyoming_openwakeword/openwakeword.py | 397 ------------------ wyoming_openwakeword/state.py | 43 -- 9 files changed, 186 insertions(+), 656 deletions(-) create mode 100644 wyoming_openwakeword/models/timer_v0.1.tflite create mode 100644 wyoming_openwakeword/models/weather_v0.1.tflite delete mode 100644 wyoming_openwakeword/openwakeword.py delete mode 100644 wyoming_openwakeword/state.py diff --git a/requirements.txt b/requirements.txt index 7687a6f..ee6b22d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ -tflite-runtime +openwakeword==0.6.0 wyoming==1.5.3 numpy<2 diff --git a/tests/test_openwakeword.py b/tests/test_openwakeword.py index 6f9148b..29a9c99 100644 --- a/tests/test_openwakeword.py +++ b/tests/test_openwakeword.py @@ -58,7 +58,7 @@ async def test_openwakeword() -> None: model_found = True break - assert model_found, "Expected 'ok nabu' model" + assert model_found, f"Expected 'ok nabu' model in {wake.models}" break # We want to use the 'ok nabu' model diff --git a/wyoming_openwakeword/VERSION b/wyoming_openwakeword/VERSION index 81c871d..227cea2 100644 --- a/wyoming_openwakeword/VERSION +++ b/wyoming_openwakeword/VERSION @@ -1 +1 @@ -1.10.0 +2.0.0 diff --git a/wyoming_openwakeword/__main__.py b/wyoming_openwakeword/__main__.py index 968e9b2..13ecdfc 100644 --- a/wyoming_openwakeword/__main__.py +++ b/wyoming_openwakeword/__main__.py @@ -4,14 +4,12 @@ import logging from functools import partial from pathlib import Path -from threading import Thread +import openwakeword from wyoming.server import AsyncServer from . import __version__ -from .handler import OpenWakeWordEventHandler, ensure_loaded -from .openwakeword import embeddings_proc, mels_proc -from .state import State +from .handler import OpenWakeWordEventHandler _LOGGER = logging.getLogger() _DIR = Path(__file__).parent @@ -19,39 +17,32 @@ async def main() -> None: parser = argparse.ArgumentParser() - parser.add_argument("--uri", default="stdio://", help="unix:// or tcp://") parser.add_argument( "--models-dir", default=_DIR / "models", help="Path to directory with built-in models", ) + parser.add_argument("--uri", default="stdio://", help="unix:// or tcp://") parser.add_argument( "--custom-model-dir", action="append", default=[], help="Path to directory with custom wake word models", ) - parser.add_argument( - "--preload-model", - action="append", - default=[], - help="Name or path of wake word model(s) to pre-load", - ) parser.add_argument( "--threshold", type=float, default=0.5, help="Wake word model threshold (0.0-1.0, default: 0.5)", ) + parser.add_argument("--output-dir", help="Path to save audio and detections") parser.add_argument( - "--trigger-level", - type=int, - default=1, - help="Number of activations before detection (default: 1)", + "--refractory-seconds", + type=float, + default=0.5, + help="Seconds before the same wake word can be triggered again", ) # - parser.add_argument("--output-dir", help="Path to save audio and detections") - # parser.add_argument("--debug", action="store_true", help="Log DEBUG messages") parser.add_argument( "--log-format", default=logging.BASIC_FORMAT, help="Format for log messages" @@ -62,8 +53,6 @@ async def main() -> None: help="Log all wake word probabilities (VERY noisy)", ) parser.add_argument("--version", action="store_true", help="Print version and exit") - # - parser.add_argument("--model", action="append", default=[], help="Deprecated") args = parser.parse_args() @@ -85,51 +74,38 @@ async def main() -> None: args.output_dir.mkdir(parents=True, exist_ok=True) _LOGGER.info("Audio will be saved to %s", args.output_dir) - # Resolve wake word model paths - state = State( - models_dir=Path(args.models_dir), - custom_model_dirs=[Path(d) for d in args.custom_model_dir], - debug_probability=args.debug_probability, - output_dir=args.output_dir, - ) - - # Pre-load models - ensure_loaded( - state, - args.preload_model, - threshold=args.threshold, - trigger_level=args.trigger_level, - ) + models_dir = Path(args.models_dir) - # audio -> mels - mels_thread = Thread(target=mels_proc, daemon=True, args=(state,)) - mels_thread.start() + # Patch model paths + for model_dict in ( + openwakeword.FEATURE_MODELS, + openwakeword.VAD_MODELS, + openwakeword.MODELS, + ): + for model_value in model_dict.values(): + model_path = Path(model_value["model_path"]) + model_path = models_dir / model_path.name + model_value["model_path"] = str(model_path) - # mels -> embeddings - embeddings_thread = Thread(target=embeddings_proc, daemon=True, args=(state,)) - embeddings_thread.start() _LOGGER.info("Ready") # Start server server = AsyncServer.from_uri(args.uri) try: - await server.run(partial(OpenWakeWordEventHandler, args, state)) + await server.run( + partial( + OpenWakeWordEventHandler, + models_dir, + [Path(d) for d in args.custom_model_dir], + args.threshold, + args.refractory_seconds, + Path(args.output_dir) if args.output_dir else None, + args.debug_probability, + ) + ) except KeyboardInterrupt: pass - finally: - # Graceful shutdown - _LOGGER.debug("Shutting down") - state.is_running = False - state.audio_ready.release() - mels_thread.join() - - state.mels_ready.release() - embeddings_thread.join() - - for ww_name, ww_state in state.wake_words.items(): - ww_state.embeddings_ready.release() - state.ww_threads[ww_name].join() # ----------------------------------------------------------------------------- diff --git a/wyoming_openwakeword/handler.py b/wyoming_openwakeword/handler.py index 3eed865..d753465 100644 --- a/wyoming_openwakeword/handler.py +++ b/wyoming_openwakeword/handler.py @@ -1,50 +1,81 @@ """Event handler for clients of the server.""" -import argparse -import asyncio import logging import re import time import wave +from collections import defaultdict from pathlib import Path -from threading import Thread -from typing import List, Optional +from threading import Lock +from typing import Dict, FrozenSet, List, Optional, Set import numpy as np +import openwakeword +from openwakeword.model import Model from wyoming.audio import AudioChunk, AudioChunkConverter, AudioStart, AudioStop from wyoming.event import Event from wyoming.info import Attribution, Describe, Info, WakeModel, WakeProgram from wyoming.server import AsyncEventHandler -from wyoming.wake import Detect, NotDetected +from wyoming.wake import Detect, Detection, NotDetected from . import __version__ -from .const import EMB_FEATURES, MEL_SAMPLES, ClientData, WakeWordData -from .openwakeword import ww_proc -from .state import State, WakeWordState _LOGGER = logging.getLogger(__name__) _WAKE_WORD_WITH_VERSION = re.compile(r"^(.+)_(v[0-9.]+)$") +_CACHED_MODELS: Dict[FrozenSet[str], List[Model]] = defaultdict(list) +_CACHED_MODELS_LOCK = Lock() + class OpenWakeWordEventHandler(AsyncEventHandler): """Event handler for openWakeWord clients.""" def __init__( self, - cli_args: argparse.Namespace, - state: State, + builtin_models_dir: Path, + custom_model_dirs: List[Path], + threshold: float, + refractory_seconds: float, + output_dir: Optional[Path], + debug_probability: bool, *args, **kwargs, ) -> None: super().__init__(*args, **kwargs) - self.cli_args = cli_args + self.builtin_models_dir = builtin_models_dir + self.custom_model_dirs = custom_model_dirs + self.threshold = threshold + self.output_dir = output_dir + self.refractory_seconds = refractory_seconds + self.debug_probability = debug_probability + self.client_id = str(time.monotonic_ns()) - self.state = state - self.data: Optional[ClientData] = None + + # openWakeWord object. + # Pulled from cache if possible (_CACHED_MODELS). + self.model: Optional[Model] = None + + # Key for model in cache. + # This is the set of model paths that are loaded. + self.model_cache_key: Optional[FrozenSet[str]] = None + + # Timestamp when a model can be triggered again + self.model_wait_time: Dict[str, float] = {} + + # Audio resampler self.converter = AudioChunkConverter(rate=16000, width=2, channels=1) - self.audio_buffer = bytes() - # Only used when output_dir is set + # Timestamp of most recent audio chunk + self.last_timestamp: Optional[int] = None + + # True if at least one detection occurred between audio start/stop + self.has_detections = False + + # Model names to listen for (empty = all) + self.detect_names: Set[str] = set() + + # Saves audio for debugging. + # Only used when output_dir is set. self.audio_writer: Optional[wave.Wave_write] = None _LOGGER.debug("Client connected: %s", self.client_id) @@ -56,45 +87,47 @@ async def handle_event(self, event: Event) -> bool: _LOGGER.debug("Sent info to client: %s", self.client_id) return True - if self.data is None: - # Create buffers for this client - self.data = ClientData(self) - with self.state.clients_lock: - self.state.clients[self.client_id] = self.data - for ww_name in self.state.wake_words: - self.data.wake_words[ww_name] = WakeWordData( - threshold=self.cli_args.threshold, - trigger_level=self.cli_args.trigger_level, - ) - if Detect.is_type(event.type): detect = Detect.from_event(event) if detect.names: - ensure_loaded( - self.state, - detect.names, - threshold=self.cli_args.threshold, - trigger_level=self.cli_args.trigger_level, - ) - - # Only process audio with these wake word models - self.data.wake_word_names = set( - self.state.wake_word_aliases.get(ww_name, ww_name) - for ww_name in detect.names - ) + # Only detect these wake word models + self.detect_names.update(detect.names) elif AudioStart.is_type(event.type): - # Reset - for ww_data in self.data.wake_words.values(): - ww_data.is_detected = False + if self.model is None: + # Try to load from cache first + wakeword_models = [str(p) for p in self._get_wakeword_model_paths()] + self.model_cache_key = frozenset(wakeword_models) + with _CACHED_MODELS_LOCK: + cached_models = _CACHED_MODELS.get(self.model_cache_key) + if cached_models: + self.model = cached_models.pop() + + if self.model is None: + # Load openWakeWord models + _LOGGER.debug("Loading openWakeWord models: %s", wakeword_models) + self.model = Model( + wakeword_models=wakeword_models, + inference_framework="tflite", + melspec_model_path=openwakeword.FEATURE_MODELS[ + "melspectrogram" + ]["model_path"], + embedding_model_path=openwakeword.FEATURE_MODELS["embedding"][ + "model_path" + ], + ) - with self.state.audio_lock: - self.data.reset() + # Reset + self.model.reset() + self.last_timestamp = None + self.has_detections = False + self.detect_names.clear() + self.model_wait_time.clear() _LOGGER.debug("Receiving audio from client: %s", self.client_id) - if self.cli_args.output_dir is not None: + if self.output_dir is not None: audio_start = AudioStart.from_event(event) - audio_path = Path(self.cli_args.output_dir) / f"{self.client_id}.wav" + audio_path = Path(self.output_dir) / f"{self.client_id}.wav" self.audio_writer = wave.open(str(audio_path), "wb") self.audio_writer.setframerate(audio_start.rate) self.audio_writer.setsampwidth(audio_start.width) @@ -102,62 +135,52 @@ async def handle_event(self, event: Event) -> bool: _LOGGER.debug("Saving audio to %s", audio_path) elif AudioChunk.is_type(event.type): - # Add to audio buffer and signal mels thread + assert self.model is not None chunk = self.converter.convert(AudioChunk.from_event(event)) + self.last_timestamp = chunk.timestamp if self.audio_writer is not None: self.audio_writer.writeframes(chunk.audio) chunk_array = np.frombuffer(chunk.audio, dtype=np.int16).astype(np.float32) - with self.state.audio_lock: - # Shift samples left - self.data.audio[: -len(chunk_array)] = self.data.audio[ - len(chunk_array) : - ] - - # Add new samples to end - self.data.audio[-len(chunk_array) :] = chunk_array - self.data.new_audio_samples = min( - len(self.data.audio), - self.data.new_audio_samples + len(chunk_array), - ) + self.model.predict(chunk_array) + for model_name in self.model.prediction_buffer: + if self.detect_names and (model_name not in self.detect_names): + _LOGGER.debug("Skipping detection of %s", model_name) + continue + + scores = list(self.model.prediction_buffer[model_name]) + if scores[-1] > self.threshold: + model_time = self.model_wait_time.get(model_name) + if (model_time is not None) and (time.monotonic() < model_time): + # Within refractory period + continue + + self.model_wait_time[model_name] = ( + time.monotonic() + self.refractory_seconds + ) + _LOGGER.debug("Detected: %s", model_name) + await self.write_event( + Detection( + name=model_name, timestamp=self.last_timestamp + ).event() + ) - self.data.audio_timestamp = chunk.timestamp or time.monotonic_ns() + if self.debug_probability: + _LOGGER.debug("%s: %s", model_name, scores[-1]) - # Signal mels thread that audio is ready to process - self.state.audio_ready.release() elif AudioStop.is_type(event.type): # Inform client if no detections occurred - while True: - if ( - (self.data.new_audio_samples < MEL_SAMPLES) - and (self.data.new_mels < EMB_FEATURES) - and all( - (ww_data.ww_windows is not None) - and (ww_data.new_embeddings < ww_data.ww_windows) - and (not ww_data.is_processing) - for ww_data in self.data.wake_words.values() - ) - ): - break - - # Wait until no new embeddings still need to be processed - await asyncio.sleep(0.1) - - if not any( - ww_data.is_detected for ww_data in self.data.wake_words.values() - ): - # No wake word detections + if not self.has_detections: await self.write_event(NotDetected().event()) _LOGGER.debug( "Audio stopped without detection from client: %s", self.client_id ) - if self.audio_writer is not None: - self.audio_writer.close() - self.audio_writer = None + self._close_audio_writer() + self._return_model_to_cache() else: _LOGGER.debug("Unexpected event: type=%s, data=%s", event.type, event.data) @@ -166,15 +189,56 @@ async def handle_event(self, event: Event) -> bool: async def disconnect(self) -> None: _LOGGER.debug("Client disconnected: %s", self.client_id) + self._close_audio_writer() + self._return_model_to_cache() + + def _close_audio_writer(self) -> None: if self.audio_writer is not None: self.audio_writer.close() self.audio_writer = None - if self.data is None: - return + def _return_model_to_cache(self) -> None: + if (self.model is not None) and (self.model_cache_key is not None): + with _CACHED_MODELS_LOCK: + _CACHED_MODELS[self.model_cache_key].append(self.model) + + self.model = None + self.model_cache_key = None + + def _get_wakeword_model_paths(self) -> List[Path]: + model_paths = self._get_model_paths() + if not self.detect_names: + # All models + return model_paths + + detect_model_paths: List[Path] = [] + for model_name in self.detect_names: + norm_model_name = _normalize_key(model_name) + for maybe_model_path in model_paths: + if norm_model_name == _normalize_key(maybe_model_path.stem): + # Exact match + detect_model_paths.append(maybe_model_path) + break + + if match := _WAKE_WORD_WITH_VERSION.match(maybe_model_path.stem): + # Exclude version + if norm_model_name == _normalize_key(match.group(1)): + detect_model_paths.append(maybe_model_path) + break + + return detect_model_paths + + def _get_model_paths(self) -> List[Path]: + model_paths: List[Path] = [ + p + for p in self.builtin_models_dir.glob("*.tflite") + if _WAKE_WORD_WITH_VERSION.match(p.stem) + ] + + for custom_model_dir in self.custom_model_dirs: + model_paths.extend(custom_model_dir.glob("*.tflite")) - with self.state.clients_lock: - self.state.clients.pop(self.client_id, None) + return model_paths def _get_info(self) -> Info: return Info( @@ -201,7 +265,7 @@ def _get_info(self) -> Info: languages=[], version=_get_version(model_path.stem), ) - for model_path in _get_wake_word_files(self.state) + for model_path in self._get_model_paths() ], ) ], @@ -211,76 +275,6 @@ def _get_info(self) -> Info: # ----------------------------------------------------------------------------- -def ensure_loaded(state: State, names: List[str], threshold: float, trigger_level: int): - """Ensure wake words are loaded by name.""" - with state.clients_lock, state.ww_threads_lock: - for model_name in names: - norm_model_name = _normalize_key(model_name) - - ww_state = state.wake_words.get(model_name) - if ww_state is not None: - # Already loaded - continue - - model_paths = _get_wake_word_files(state) - model_path: Optional[Path] = None - for maybe_model_path in model_paths: - if norm_model_name == _normalize_key(maybe_model_path.stem): - # Exact match - model_path = maybe_model_path - break - - if match := _WAKE_WORD_WITH_VERSION.match(maybe_model_path.stem): - # Exclude version - if norm_model_name == _normalize_key(match.group(1)): - model_path = maybe_model_path - state.wake_word_aliases[model_name] = model_path.stem - break - - if model_path is None: - raise ValueError(f"Wake word model not found: {model_name}") - - # Start thread for model - model_key = model_path.stem - state.wake_words[model_key] = WakeWordState() - state.ww_threads[model_key] = Thread( - target=ww_proc, - daemon=True, - args=( - state, - model_key, - model_path, - asyncio.get_running_loop(), - ), - ) - state.ww_threads[model_key].start() - - for client_data in state.clients.values(): - client_data.wake_words[model_key] = WakeWordData( - threshold=threshold, - trigger_level=trigger_level, - ) - - _LOGGER.debug("Started thread for %s", model_key) - - -# ----------------------------------------------------------------------------- - - -def _get_wake_word_files(state: State) -> List[Path]: - """Get paths to all available wake word model files.""" - model_paths = [ - p - for p in state.models_dir.glob("*.tflite") - if _WAKE_WORD_WITH_VERSION.match(p.stem) - ] - - for custom_model_dir in state.custom_model_dirs: - model_paths.extend(custom_model_dir.glob("*.tflite")) - - return model_paths - - def _normalize_key(model_key: str) -> str: """Normalize model key for comparison.""" return model_key.lower().replace("_", " ").strip() diff --git a/wyoming_openwakeword/models/timer_v0.1.tflite b/wyoming_openwakeword/models/timer_v0.1.tflite new file mode 100644 index 0000000000000000000000000000000000000000..3005d8c1f6098ce1696d18a57ff34914a22a17ea GIT binary patch literal 1743316 zcmZ^~2RK*n8$bT0K`IR-qLiW`A{E}xO_3_@AKR$N!l9P z+LLw~TK>o9^Zj1`-}V3g{^z=0*K^K&p7DC-x$oz>j}(W)*}B1Sr6@;+GoB;JnZlXQ z5oKkwIa2?GOuohh@d#$gmhI!_UPx(B0e3-r2=hj z|Kwx8b9aF6f6Cm?N!a;fbd1+r7Q4N@gY5lX?Z>deYlpwTi?{s>d&kwQomQ=K-0^R{ 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zb>oL(w%kq9cy$)C?O(+U!#c$t&3{D)OAn*o32%XKfD;3ik*s=90N}$nWcGd^kxXVL z{B9#qX*dM1F_Q(~u(Q%PK8K^drQoBX11$SB32xLz!^UZstd3YCR^;fyQ|~T#sH22< z!X4@h_G4eOM*>EHKu7C(<`8WpGX9h(3jVTy-I$h2o-~Kzp#d{+MP)g@JGY!{HuuCG zh5Oj8QQ1W8+;4{I40U(ap~R@gqFeGav1*?Zt`q9)y^Q49@17juB0COV+P)D9r zhlS&MqgP)cQuW5RUjGronS}6mPj6h4kQgAus=k7+DG1}J39bM9NcE1{8~=J=^#$vV zWxZ`h!F&Uk1qb^CjsH(f6WYBa|Hm(d-fI6lL+@z4WA*m$?b{m^_v2M{5)6q_%7QR80@(wC}@jku&-yYP5bY;JWV`JtW1}!ST@mD)Z0ZuxE+Lh Kq4!rp;{O1Bj(y7j literal 0 HcmV?d00001 diff --git a/wyoming_openwakeword/openwakeword.py b/wyoming_openwakeword/openwakeword.py deleted file mode 100644 index bbcf1fb..0000000 --- a/wyoming_openwakeword/openwakeword.py +++ /dev/null @@ -1,397 +0,0 @@ -import asyncio -import logging -from datetime import datetime -from typing import Dict, List, Optional, TextIO - -import numpy as np - -try: - import tflite_runtime.interpreter as tflite -except ModuleNotFoundError: - import tensorflow.lite as tflite -from wyoming.wake import Detection - -from .const import ( - EMB_FEATURES, - EMB_STEP, - MEL_SAMPLES, - MS_PER_CHUNK, - NUM_MELS, - SAMPLES_PER_CHUNK, - WW_FEATURES, -) -from .state import ClientData, State - -_MS_PER_CHUNK = SAMPLES_PER_CHUNK // 16 - -_LOGGER = logging.getLogger() - - -def mels_proc(state: State): - """Transform audio into mel spectrograms.""" - try: - melspec_model_path = state.models_dir / "melspectrogram.tflite" - _LOGGER.debug("Loading %s", melspec_model_path) - melspec_model = tflite.Interpreter( - model_path=str(melspec_model_path), num_threads=1 - ) - melspec_input_index = melspec_model.get_input_details()[0]["index"] - melspec_output_index = melspec_model.get_output_details()[0]["index"] - - # melspec = [batch x samples (min: 1280)] => [batch x 1 x window x mels (32)] - # stft window size: 25ms (400) - # stft window step: 10ms (160) - # mel band limits: 60Hz - 3800Hz - # mel frequency bins: 32 - - client: Optional[ClientData] = None - - while state.is_running: - state.audio_ready.acquire() - if not state.is_running: - break - - while True: - with state.clients_lock, state.audio_lock: - # Collect batch - todo_ids = [ - client_id - for client_id, client in state.clients.items() - if client.new_audio_samples >= MEL_SAMPLES - ] - batch_size = len(todo_ids) - if batch_size < 1: - # Not enough audio to process - break - - audio_tensor = np.zeros( - shape=(batch_size, MEL_SAMPLES), dtype=np.float32 - ) - - todo_timestamps: List[int] = [] - for i, client_id in enumerate(todo_ids): - client = state.clients[client_id] - audio_tensor[i, :] = client.audio[ - -client.new_audio_samples : len(client.audio) - - client.new_audio_samples - + MEL_SAMPLES - ] - client.new_audio_samples = max( - 0, client.new_audio_samples - SAMPLES_PER_CHUNK - ) - todo_timestamps.append(client.audio_timestamp) - - # Shift timestamp - client.audio_timestamp += MS_PER_CHUNK - - melspec_model.resize_tensor_input( - melspec_input_index, - audio_tensor.shape, - strict=True, - ) - melspec_model.allocate_tensors() - - # Generate mels - melspec_model.set_tensor(melspec_input_index, audio_tensor) - melspec_model.invoke() - mels = melspec_model.get_tensor(melspec_output_index) - mels = (mels / 10) + 2 # transform to fit embedding - - num_mel_windows = mels.shape[2] - with state.clients_lock, state.mels_lock: - # Add to client mels - for i, client_id in enumerate(todo_ids): - client = state.clients.get(client_id) - if client is None: - # Client disconnected - continue - - # Shift - client.mels[:-num_mel_windows] = client.mels[num_mel_windows:] - - # Overwrite - client.mels[-num_mel_windows:] = mels[i, 0, :, :] - client.new_mels = min( - len(client.mels), client.new_mels + num_mel_windows - ) - - # Update timestamp - client.mels_timestamp = todo_timestamps[i] - - state.mels_ready.release() - - except Exception: - _LOGGER.exception("Unexpected error in mels thread") - - -# ----------------------------------------------------------------------------- - - -def embeddings_proc(state: State): - """Transform mels to embedding features.""" - try: - embedding_model_path = state.models_dir / "embedding_model.tflite" - _LOGGER.debug("Loading %s", embedding_model_path) - embedding_model = tflite.Interpreter( - model_path=str(embedding_model_path), num_threads=1 - ) - embedding_input_index = embedding_model.get_input_details()[0]["index"] - embedding_output_index = embedding_model.get_output_details()[0]["index"] - - # embedding = [batch x window x mels (32) x 1] => [batch x 1 x 1 x features (96)] - - client: Optional[ClientData] = None - - while state.is_running: - state.mels_ready.acquire() - if not state.is_running: - break - - while True: - with state.clients_lock, state.mels_lock: - # Collect batch - todo_ids = [ - client_id - for client_id, client in state.clients.items() - if client.new_mels >= EMB_FEATURES - ] - batch_size = len(todo_ids) - if batch_size < 1: - # Not enough audio to process - break - - mels_tensor = np.zeros( - shape=(batch_size, EMB_FEATURES, NUM_MELS, 1), - dtype=np.float32, - ) - - todo_timestamps: List[int] = [] - for i, client_id in enumerate(todo_ids): - client = state.clients[client_id] - mels_tensor[i, :, :, 0] = client.mels[ - -client.new_mels : len(client.mels) - - client.new_mels - + EMB_FEATURES, - :, - ] - client.new_mels = max(0, client.new_mels - EMB_STEP) - todo_timestamps.append(client.mels_timestamp) - - # Shift timestamp - client.mels_timestamp += MS_PER_CHUNK - - embedding_model.resize_tensor_input( - embedding_input_index, - mels_tensor.shape, - strict=True, - ) - embedding_model.allocate_tensors() - - # Generate embeddings - embedding_model.set_tensor(embedding_input_index, mels_tensor) - embedding_model.invoke() - - embeddings = embedding_model.get_tensor(embedding_output_index) - - num_embedding_windows = embeddings.shape[2] - for ww_name, ww_state in state.wake_words.items(): - with state.clients_lock, ww_state.embeddings_lock: - # Add to wake word model embeddings - for i, client_id in enumerate(todo_ids): - client = state.clients.get(client_id) - if client is None: - # Client disconnected - continue - - if client.wake_word_names and ( - ww_name not in client.wake_word_names - ): - # Skip processing wake words that won't be used - continue - - # Shift - client_data = client.wake_words[ww_name] - client_data.embeddings[ - :-num_embedding_windows - ] = client_data.embeddings[num_embedding_windows:] - - # Overwrite - client_data.embeddings[ - -num_embedding_windows: - ] = embeddings[i, 0, :, :] - client_data.new_embeddings = min( - len(client_data.embeddings), - client_data.new_embeddings + num_embedding_windows, - ) - - # Update timestamp - client_data.embeddings_timestamp = todo_timestamps[i] - - ww_state.embeddings_ready.release() - - except Exception: - _LOGGER.exception("Unexpected error in embeddings thread") - - -# ----------------------------------------------------------------------------- - - -def ww_proc( - state: State, - ww_model_key: str, - ww_model_path: str, - loop: asyncio.AbstractEventLoop, -): - """Transform embedding features to wake word probabilities (without batching).""" - try: - _LOGGER.debug("Loading %s from %s", ww_model_key, ww_model_path) - ww_model = tflite.Interpreter(model_path=str(ww_model_path), num_threads=1) - ww_input = ww_model.get_input_details()[0] - ww_input_shape = ww_input["shape"] - ww_windows = ww_input_shape[1] - ww_input_index = ww_input["index"] - ww_output_index = ww_model.get_output_details()[0]["index"] - - # ww = [batch x window x features (96)] => [batch x probability] - - client: Optional[ClientData] = None - - ww_state = state.wake_words[ww_model_key] - while state.is_running: - ww_state.embeddings_ready.acquire() - if not state.is_running: - break - - while True: - todo_timestamps: Dict[str, int] = {} - todo_embeddings: Dict[str, np.ndarray] = {} - - with state.clients_lock, ww_state.embeddings_lock: - for client_id, client in state.clients.items(): - client_data = client.wake_words[ww_model_key] - - if client_data.ww_windows is None: - # Number of feature windows needed for this model - client_data.ww_windows = ww_windows - - if client_data.new_embeddings < ww_windows: - continue - - client_data.is_processing = True - embeddings_tensor = np.zeros( - shape=(1, ww_windows, WW_FEATURES), - dtype=np.float32, - ) - - embeddings_tensor[0, :] = client_data.embeddings[ - -client_data.new_embeddings : len(client_data.embeddings) - - client_data.new_embeddings - + ww_windows - ] - client_data.new_embeddings = max( - 0, client_data.new_embeddings - 1 - ) - todo_timestamps[client_id] = client_data.embeddings_timestamp - - # Shift timestamp - client_data.embeddings_timestamp += MS_PER_CHUNK - - todo_embeddings[client_id] = embeddings_tensor - - if not todo_embeddings: - break - - for client_id, embeddings_tensor in todo_embeddings.items(): - ww_model.resize_tensor_input( - ww_input_index, - embeddings_tensor.shape, - strict=False, # must be False for non-dynamic batch dim - ) - ww_model.allocate_tensors() - - # Generate probabilities - ww_model.set_tensor(ww_input_index, embeddings_tensor) - ww_model.invoke() - probabilities = ww_model.get_tensor(ww_output_index) - probability = probabilities[0] - - coros = [] - with state.clients_lock: - client = state.clients.get(client_id) - if client is None: - # Client disconnected - continue - - if state.debug_probability: - _LOGGER.debug( - "client=%s, wake_word=%s, probability=%s", - client_id, - ww_model_key, - probability.item(), - ) - - prob_file: Optional[TextIO] = None - if (state.output_dir is not None) and state.debug_probability: - # Output chunk probabilities and detections for debugging - prob_file = open( - state.output_dir / f"{client_id}.txt", - "a", - encoding="utf-8", - ) - print( - _timestamp(), - ww_model_key, - probability.item(), - file=prob_file, - ) - - client_data = client.wake_words[ww_model_key] - if probability.item() >= client_data.threshold: - # Increase activation - client_data.activations += 1 - - if client_data.activations >= client_data.trigger_level: - client_data.is_detected = True - client_data.activations = 0 - coros.append( - client.event_handler.write_event( - Detection( - name=ww_model_key, - timestamp=todo_timestamps[client_id], - ).event() - ), - ) - _LOGGER.debug( - "Triggered %s (client=%s)", ww_model_key, client_id - ) - - if prob_file is not None: - print( - _timestamp(), - ww_model_key, - "detected", - file=prob_file, - ) - else: - # Down towards 0 - client_data.activations = max( - 0, client_data.activations - 1 - ) - - # Clean up - if prob_file is not None: - prob_file.close() - prob_file = None - - # Run outside lock just to be safe - for coro in coros: - asyncio.run_coroutine_threadsafe(coro, loop) - - client_data.is_processing = False - - except Exception: - _LOGGER.exception("Unexpected error in wake word thread (%s)", ww_model_key) - - -def _timestamp() -> str: - return datetime.now().strftime("%H:%M:%S.%f") diff --git a/wyoming_openwakeword/state.py b/wyoming_openwakeword/state.py deleted file mode 100644 index 392cd27..0000000 --- a/wyoming_openwakeword/state.py +++ /dev/null @@ -1,43 +0,0 @@ -from dataclasses import dataclass, field -from pathlib import Path -from threading import Lock, Semaphore, Thread -from typing import Dict, List, Optional - -from .const import ClientData - - -@dataclass -class WakeWordState: - embeddings_ready: Semaphore = field(default_factory=Semaphore) - embeddings_lock: Lock = field(default_factory=Lock) - - -@dataclass -class State: - models_dir: Path - """Directory with built-in models.""" - - custom_model_dirs: List[Path] = field(default_factory=list) - """Directories with custom wake word models.""" - - ww_threads: Dict[str, Thread] = field(default_factory=dict) - ww_threads_lock: Lock = field(default_factory=Lock) - - is_running: bool = True - clients: Dict[str, ClientData] = field(default_factory=dict) - clients_lock: Lock = field(default_factory=Lock) - - audio_ready: Semaphore = field(default_factory=Semaphore) - audio_lock: Lock = field(default_factory=Lock) - - mels_ready: Semaphore = field(default_factory=Semaphore) - mels_lock: Lock = field(default_factory=Lock) - - # full name -> state - wake_words: Dict[str, WakeWordState] = field(default_factory=dict) - - # alias -> full name - wake_word_aliases: Dict[str, str] = field(default_factory=dict) - - debug_probability: bool = False - output_dir: Optional[Path] = None

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