headmic/sound_id.py

"""
Sound Identification Module for HeadMic
YAMNet audio event classifier — CPU TFLite (Edge TPU ready)
"""

import collections
import csv
import logging
import time

import numpy as np

logger = logging.getLogger("sound_id")
logger.setLevel(logging.INFO)

# YAMNet expects 0.975s of 16kHz audio = 15600 samples
YAMNET_SAMPLE_RATE = 16000
YAMNET_INPUT_SAMPLES = 15600

# Category mapping: group YAMNet's 521 classes into useful buckets
CATEGORY_GROUPS = {
    "speech": [
        "Speech", "Child speech, kid speaking", "Conversation",
        "Narration, monologue", "Babbling", "Speech synthesizer",
        "Shout", "Yell", "Whispering", "Laughter", "Chatter",
    ],
    "alert": [
        "Doorbell", "Ding-dong", "Knock", "Tap", "Alarm",
        "Alarm clock", "Smoke detector, smoke alarm", "Fire alarm",
        "Siren", "Buzzer", "Telephone", "Telephone bell ringing",
        "Ringtone", "Telephone dialing, DTMF", "Bell", "Church bell",
    ],
    "music": [
        "Music", "Musical instrument", "Singing", "Song", "Guitar",
        "Electric guitar", "Acoustic guitar", "Bass guitar", "Piano",
        "Keyboard (musical)", "Drum", "Drum kit", "Snare drum",
        "Bass drum", "Cymbal", "Hi-hat", "Violin, fiddle", "Flute",
        "Trumpet", "Saxophone", "Harmonica", "Organ", "Synthesizer",
        "Plucked string instrument", "Strum", "Hip hop music",
        "Pop music", "Rock music", "Heavy metal", "Punk rock",
        "Grunge", "Progressive rock", "Rock and roll", "Jazz",
        "Blues", "Soul music", "Reggae", "Country", "Swing music",
        "Bluegrass", "Funk", "Folk music", "Middle Eastern music",
        "Electronic music", "House music", "Techno", "Dubstep",
        "Drum and bass", "Electronica", "Electronic dance music",
        "Ambient music", "Trance music", "Music of Latin America",
        "Salsa music", "Flamenco", "Gospel music", "Christian music",
        "Music of Africa", "Afrobeat", "Music of Asia",
    ],
    "animal": [
        "Dog", "Bark", "Howl", "Bow-wow", "Growling", "Whimper",
        "Cat", "Purr", "Meow", "Hiss", "Caterwaul",
        "Bird", "Bird vocalization, bird call, bird song", "Chirp, tweet",
        "Squawk", "Crow", "Caw", "Owl", "Pigeon, dove",
        "Insect", "Cricket", "Mosquito", "Fly, housefly", "Bee, wasp, etc.",
        "Frog", "Rooster", "Chicken, hen", "Duck", "Goose",
    ],
    "household": [
        "Door", "Doorbell", "Sliding door", "Slam",
        "Drawer open or close", "Cupboard open or close",
        "Cutlery, silverware", "Dishes, pots, and pans",
        "Glass", "Chink, clink", "Water tap, faucet",
        "Sink (filling or washing)", "Bathtub (filling or washing)",
        "Toilet flush", "Vacuum cleaner", "Blender",
        "Microwave oven", "Typing", "Computer keyboard",
        "Writing", "Keys jangling", "Coin (dropping)",
        "Footsteps", "Walk, footsteps", "Run",
        "Clapping", "Finger snapping",
    ],
    "environment": [
        "Wind", "Rustling leaves", "Wind noise (microphone)",
        "Rain", "Raindrop", "Rain on surface", "Thunder",
        "Thunderstorm", "Stream", "Waterfall", "Ocean",
        "Waves, surf", "Traffic noise, roadway noise",
        "Car", "Engine", "Idling", "Truck", "Bus", "Motorcycle",
        "Aircraft", "Aircraft engine", "Helicopter",
        "Train", "Railroad car, train wagon",
    ],
    "silence": [
        "Silence", "White noise", "Static",
    ],
}


class SoundClassifier:
    def __init__(self, model_path, class_map_path, use_edgetpu=False):
        # Load class names
        self._class_names = []
        with open(class_map_path) as f:
            reader = csv.reader(f)
            next(reader)  # skip header
            for row in reader:
                self._class_names.append(row[2])
        logger.info("Loaded %d YAMNet class names", len(self._class_names))

        # Build reverse lookup: class_name -> category
        self._class_to_category = {}
        for category, names in CATEGORY_GROUPS.items():
            for name in names:
                self._class_to_category[name] = category

        # Load TFLite model
        import ai_edge_litert.interpreter as tfl

        if use_edgetpu:
            delegate = tfl.load_delegate("libedgetpu.so.1")
            self._interp = tfl.Interpreter(
                model_path=str(model_path),
                experimental_delegates=[delegate],
            )
            logger.info("YAMNet loaded on Edge TPU")
        else:
            self._interp = tfl.Interpreter(model_path=str(model_path))
            logger.info("YAMNet loaded on CPU")

        self._interp.allocate_tensors()
        self._input = self._interp.get_input_details()[0]
        self._output = self._interp.get_output_details()[0]
        logger.info(
            "YAMNet ready: input %s %s, output %s",
            self._input["shape"], self._input["dtype"], self._output["shape"],
        )

        # Classification history for smoothing
        self._history = collections.deque(maxlen=20)

    def classify(self, audio_int16):
        """Classify an audio buffer.

        Args:
            audio_int16: numpy int16 array of PCM samples at 16kHz

        Returns:
            dict with category, top_classes, dominant_category, timestamp
        """
        # Convert int16 PCM to float32 [-1.0, 1.0]
        audio_f32 = audio_int16.astype(np.float32) / 32768.0

        # Pad or trim to exact input size
        if len(audio_f32) < YAMNET_INPUT_SAMPLES:
            audio_f32 = np.pad(audio_f32, (0, YAMNET_INPUT_SAMPLES - len(audio_f32)))
        elif len(audio_f32) > YAMNET_INPUT_SAMPLES:
            audio_f32 = audio_f32[-YAMNET_INPUT_SAMPLES:]

        self._interp.set_tensor(self._input["index"], audio_f32)
        self._interp.invoke()

        scores = self._interp.get_tensor(self._output["index"])[0]

        # Top 5 classes
        top_indices = np.argsort(scores)[-5:][::-1]
        top_classes = []
        for idx in top_indices:
            name = self._class_names[idx] if idx < len(self._class_names) else "Unknown"
            top_classes.append({"name": name, "score": round(float(scores[idx]), 3)})

        # Map top class to category
        top_name = top_classes[0]["name"]
        category = self._class_to_category.get(top_name, "other")

        # Update history
        now = time.time()
        self._history.append({"category": category, "timestamp": now})

        # Dominant category from recent history
        if self._history:
            cat_counts = collections.Counter(h["category"] for h in self._history)
            dominant = cat_counts.most_common(1)[0][0]
        else:
            dominant = category

        return {
            "category": category,
            "top_classes": top_classes,
            "dominant_category": dominant,
            "timestamp": now,
        }

    def get_history(self, seconds=30):
        """Return recent classification history."""
        cutoff = time.time() - seconds
        return [h for h in self._history if h["timestamp"] >= cutoff]