"""Handle creating audio events.""" import datetime import logging import threading import time from typing import Tuple import numpy as np import requests import frigate.util as util from frigate.camera import CameraMetrics from frigate.comms.config_updater import ConfigSubscriber from frigate.comms.detections_updater import DetectionPublisher, DetectionTypeEnum from frigate.comms.inter_process import InterProcessRequestor from frigate.config import CameraConfig, CameraInput, FfmpegConfig from frigate.const import ( AUDIO_DURATION, AUDIO_FORMAT, AUDIO_MAX_BIT_RANGE, AUDIO_MIN_CONFIDENCE, AUDIO_SAMPLE_RATE, FRIGATE_LOCALHOST, ) from frigate.ffmpeg_presets import parse_preset_input from frigate.log import LogPipe from frigate.object_detection import load_labels from frigate.util.builtin import get_ffmpeg_arg_list from frigate.video import start_or_restart_ffmpeg, stop_ffmpeg try: from tflite_runtime.interpreter import Interpreter except ModuleNotFoundError: from tensorflow.lite.python.interpreter import Interpreter def get_ffmpeg_command(ffmpeg: FfmpegConfig) -> list[str]: ffmpeg_input: CameraInput = [i for i in ffmpeg.inputs if "audio" in i.roles][0] input_args = get_ffmpeg_arg_list(ffmpeg.global_args) + ( parse_preset_input(ffmpeg_input.input_args, 1) or get_ffmpeg_arg_list(ffmpeg_input.input_args) or parse_preset_input(ffmpeg.input_args, 1) or get_ffmpeg_arg_list(ffmpeg.input_args) ) return ( [ffmpeg.ffmpeg_path, "-vn", "-threads", "1"] + input_args + ["-i"] + [ffmpeg_input.path] + [ "-threads", "1", "-f", f"{AUDIO_FORMAT}", "-ar", f"{AUDIO_SAMPLE_RATE}", "-ac", "1", "-y", "pipe:", ] ) class AudioProcessor(util.Process): name = "frigate.audio_manager" def __init__( self, cameras: list[CameraConfig], camera_metrics: dict[str, CameraMetrics], ): super().__init__(name="frigate.audio_manager", daemon=True) self.camera_metrics = camera_metrics self.cameras = cameras def run(self) -> None: audio_threads: list[AudioEventMaintainer] = [] threading.current_thread().name = "process:audio_manager" if len(self.cameras) == 0: return for camera in self.cameras: audio_thread = AudioEventMaintainer( camera, self.camera_metrics, self.stop_event, ) audio_threads.append(audio_thread) audio_thread.start() self.logger.info(f"Audio processor started (pid: {self.pid})") while not self.stop_event.wait(): pass for thread in audio_threads: thread.join(1) if thread.is_alive(): self.logger.info(f"Waiting for thread {thread.name:s} to exit") thread.join(10) for thread in audio_threads: if thread.is_alive(): self.logger.warning(f"Thread {thread.name} is still alive") self.logger.info("Exiting audio processor") class AudioEventMaintainer(threading.Thread): def __init__( self, camera: CameraConfig, camera_metrics: dict[str, CameraMetrics], stop_event: threading.Event, ) -> None: super().__init__(name=f"{camera.name}_audio_event_processor") self.config = camera self.camera_metrics = camera_metrics self.detections: dict[dict[str, any]] = {} self.stop_event = stop_event self.detector = AudioTfl(stop_event, self.config.audio.num_threads) self.shape = (int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE)),) self.chunk_size = int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE * 2)) self.logger = logging.getLogger(f"audio.{self.config.name}") self.ffmpeg_cmd = get_ffmpeg_command(self.config.ffmpeg) self.logpipe = LogPipe(f"ffmpeg.{self.config.name}.audio") self.audio_listener = None # create communication for audio detections self.requestor = InterProcessRequestor() self.config_subscriber = ConfigSubscriber(f"config/audio/{camera.name}") self.detection_publisher = DetectionPublisher(DetectionTypeEnum.audio) def detect_audio(self, audio) -> None: if not self.config.audio.enabled or self.stop_event.is_set(): return audio_as_float = audio.astype(np.float32) rms, dBFS = self.calculate_audio_levels(audio_as_float) self.camera_metrics[self.config.name].audio_rms.value = rms self.camera_metrics[self.config.name].audio_dBFS.value = dBFS # only run audio detection when volume is above min_volume if rms >= self.config.audio.min_volume: # create waveform relative to max range and look for detections waveform = (audio / AUDIO_MAX_BIT_RANGE).astype(np.float32) model_detections = self.detector.detect(waveform) audio_detections = [] for label, score, _ in model_detections: self.logger.debug( f"{self.config.name} heard {label} with a score of {score}" ) if label not in self.config.audio.listen: continue if score > dict((self.config.audio.filters or {}).get(label, {})).get( "threshold", 0.8 ): self.handle_detection(label, score) audio_detections.append(label) # send audio detection data self.detection_publisher.publish( ( self.config.name, datetime.datetime.now().timestamp(), dBFS, audio_detections, ) ) self.expire_detections() def calculate_audio_levels(self, audio_as_float: np.float32) -> Tuple[float, float]: # Calculate RMS (Root-Mean-Square) which represents the average signal amplitude # Note: np.float32 isn't serializable, we must use np.float64 to publish the message rms = np.sqrt(np.mean(np.absolute(np.square(audio_as_float)))) # Transform RMS to dBFS (decibels relative to full scale) if rms > 0: dBFS = 20 * np.log10(np.abs(rms) / AUDIO_MAX_BIT_RANGE) else: dBFS = 0 self.requestor.send_data(f"{self.config.name}/audio/dBFS", float(dBFS)) self.requestor.send_data(f"{self.config.name}/audio/rms", float(rms)) return float(rms), float(dBFS) def handle_detection(self, label: str, score: float) -> None: if self.detections.get(label): self.detections[label]["last_detection"] = ( datetime.datetime.now().timestamp() ) else: self.requestor.send_data(f"{self.config.name}/audio/{label}", "ON") resp = requests.post( f"{FRIGATE_LOCALHOST}/api/events/{self.config.name}/{label}/create", json={"duration": None, "score": score, "source_type": "audio"}, ) if resp.status_code == 200: event_id = resp.json()["event_id"] self.detections[label] = { "id": event_id, "label": label, "last_detection": datetime.datetime.now().timestamp(), } def expire_detections(self) -> None: now = datetime.datetime.now().timestamp() for detection in self.detections.values(): if not detection: continue if ( now - detection.get("last_detection", now) > self.config.audio.max_not_heard ): self.requestor.send_data( f"{self.config.name}/audio/{detection['label']}", "OFF" ) resp = requests.put( f"{FRIGATE_LOCALHOST}/api/events/{detection['id']}/end", json={"end_time": detection["last_detection"]}, ) if resp.status_code == 200: self.detections[detection["label"]] = None else: self.logger.warning( f"Failed to end audio event {detection['id']} with status code {resp.status_code}" ) def start_or_restart_ffmpeg(self) -> None: self.audio_listener = start_or_restart_ffmpeg( self.ffmpeg_cmd, self.logger, self.logpipe, self.chunk_size, self.audio_listener, ) def read_audio(self) -> None: def log_and_restart() -> None: if self.stop_event.is_set(): return time.sleep(self.config.ffmpeg.retry_interval) self.logpipe.dump() self.start_or_restart_ffmpeg() try: chunk = self.audio_listener.stdout.read(self.chunk_size) if not chunk: if self.audio_listener.poll() is not None: self.logger.error("ffmpeg process is not running, restarting...") log_and_restart() return return audio = np.frombuffer(chunk, dtype=np.int16) self.detect_audio(audio) except Exception as e: self.logger.error(f"Error reading audio data from ffmpeg process: {e}") log_and_restart() def run(self) -> None: self.start_or_restart_ffmpeg() while not self.stop_event.is_set(): # check if there is an updated config ( updated_topic, updated_audio_config, ) = self.config_subscriber.check_for_update() if updated_topic: self.config.audio = updated_audio_config self.read_audio() stop_ffmpeg(self.audio_listener, self.logger) self.logpipe.close() self.requestor.stop() self.config_subscriber.stop() self.detection_publisher.stop() class AudioTfl: def __init__(self, stop_event: threading.Event, num_threads=2): self.stop_event = stop_event self.num_threads = num_threads self.labels = load_labels("/audio-labelmap.txt", prefill=521) self.interpreter = Interpreter( model_path="/cpu_audio_model.tflite", num_threads=self.num_threads, ) self.interpreter.allocate_tensors() self.tensor_input_details = self.interpreter.get_input_details() self.tensor_output_details = self.interpreter.get_output_details() def _detect_raw(self, tensor_input): self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input) self.interpreter.invoke() detections = np.zeros((20, 6), np.float32) res = self.interpreter.get_tensor(self.tensor_output_details[0]["index"])[0] non_zero_indices = res > 0 class_ids = np.argpartition(-res, 20)[:20] class_ids = class_ids[np.argsort(-res[class_ids])] class_ids = class_ids[non_zero_indices[class_ids]] scores = res[class_ids] boxes = np.full((scores.shape[0], 4), -1, np.float32) count = len(scores) for i in range(count): if scores[i] < AUDIO_MIN_CONFIDENCE or i == 20: break detections[i] = [ class_ids[i], float(scores[i]), boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3], ] return detections def detect(self, tensor_input, threshold=AUDIO_MIN_CONFIDENCE): detections = [] if self.stop_event.is_set(): return detections raw_detections = self._detect_raw(tensor_input) for d in raw_detections: if d[1] < threshold: break detections.append( (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5])) ) return detections