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https://github.com/blakeblackshear/frigate.git
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Save audio scores and add audio filter config (#7185)
* Send and save score for external events * Add audio filters config * Fix access * Add docs * Cleanup
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@ -154,6 +154,12 @@ audio:
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- scream
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- speech
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- yell
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# Optional: Filters to configure detection.
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filters:
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# Label that matches label in listen config.
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speech:
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# Minimum score that triggers an audio event (default: shown below)
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threshold: 0.8
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# Optional: logger verbosity settings
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logger:
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@ -11,7 +11,13 @@ import numpy as np
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from pydantic import BaseModel, Extra, Field, parse_obj_as, validator
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from pydantic.fields import PrivateAttr
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from frigate.const import CACHE_DIR, DEFAULT_DB_PATH, REGEX_CAMERA_NAME, YAML_EXT
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from frigate.const import (
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AUDIO_MIN_CONFIDENCE,
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CACHE_DIR,
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DEFAULT_DB_PATH,
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REGEX_CAMERA_NAME,
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YAML_EXT,
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)
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from frigate.detectors import DetectorConfig, ModelConfig
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from frigate.detectors.detector_config import BaseDetectorConfig
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from frigate.ffmpeg_presets import (
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@ -334,6 +340,15 @@ class FilterConfig(FrigateBaseModel):
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)
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class AudioFilterConfig(FrigateBaseModel):
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threshold: float = Field(
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default=0.8,
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ge=AUDIO_MIN_CONFIDENCE,
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lt=1.0,
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title="Minimum detection confidence threshold for audio to be counted.",
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)
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class RuntimeFilterConfig(FilterConfig):
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mask: Optional[np.ndarray]
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raw_mask: Optional[Union[str, List[str]]]
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@ -424,6 +439,7 @@ class AudioConfig(FrigateBaseModel):
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listen: List[str] = Field(
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default=DEFAULT_LISTEN_AUDIO, title="Audio to listen for."
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)
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filters: Optional[Dict[str, AudioFilterConfig]] = Field(title="Audio filters.")
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enabled_in_config: Optional[bool] = Field(
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title="Keep track of original state of audio detection."
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)
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@ -28,6 +28,7 @@ AUDIO_DURATION = 0.975
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AUDIO_FORMAT = "s16le"
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AUDIO_MAX_BIT_RANGE = 32768.0
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AUDIO_SAMPLE_RATE = 16000
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AUDIO_MIN_CONFIDENCE = 0.5
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# Regex Consts
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@ -19,6 +19,7 @@ from frigate.const import (
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AUDIO_DURATION,
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AUDIO_FORMAT,
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AUDIO_MAX_BIT_RANGE,
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AUDIO_MIN_CONFIDENCE,
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AUDIO_SAMPLE_RATE,
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CACHE_DIR,
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FRIGATE_LOCALHOST,
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@ -130,7 +131,7 @@ class AudioTfl:
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return detections
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def detect(self, tensor_input, threshold=0.8):
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def detect(self, tensor_input, threshold=AUDIO_MIN_CONFIDENCE):
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detections = []
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if self.stop_event.is_set():
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@ -200,7 +201,10 @@ class AudioEventMaintainer(threading.Thread):
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if label not in self.config.audio.listen:
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continue
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self.handle_detection(label, score)
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if score > (self.config.audio.filters or {}).get(label, {}).get(
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"threshold", 0.8
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):
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self.handle_detection(label, score)
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self.expire_detections()
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@ -233,7 +237,7 @@ class AudioEventMaintainer(threading.Thread):
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resp = requests.post(
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f"{FRIGATE_LOCALHOST}/api/events/{self.config.name}/{label}/create",
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json={"duration": None, "source_type": "audio"},
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json={"duration": None, "score": score, "source_type": "audio"},
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)
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if resp.status_code == 200:
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@ -31,6 +31,7 @@ class ExternalEventProcessor:
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label: str,
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source_type: str,
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sub_label: Optional[str],
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score: int,
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duration: Optional[int],
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include_recording: bool,
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draw: dict[str, any],
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@ -56,6 +57,7 @@ class ExternalEventProcessor:
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"id": event_id,
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"label": label,
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"sub_label": sub_label,
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"score": score,
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"camera": camera,
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"start_time": now - camera_config.record.events.pre_capture,
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"end_time": now
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@ -230,7 +230,11 @@ class EventProcessor(threading.Thread):
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Event.has_clip: event_data["has_clip"],
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Event.has_snapshot: event_data["has_snapshot"],
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Event.zones: [],
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Event.data: {"type": event_data["type"]},
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Event.data: {
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"type": event_data["type"],
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"score": event_data["score"],
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"top_score": event_data["score"],
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},
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}
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Event.insert(event).execute()
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elif event_type == "end":
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@ -899,6 +899,7 @@ def create_event(camera_name, label):
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label,
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json.get("source_type", "api"),
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json.get("sub_label", None),
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json.get("score", 0),
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json.get("duration", 30),
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json.get("include_recording", True),
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json.get("draw", {}),
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