Add YAMNet sound classification to headmic

New sound_id.py module with SoundClassifier class that runs YAMNet
(521 audio event categories) on CPU TFLite. Classifies audio every
0.5s from a ring buffer fed by the existing audio stream.

Categories: speech, alert, music, animal, household, environment, silence.
Smoothing via 20-sample history window for stable dominant category.

New endpoints: GET /sounds, GET /sounds/history
Updated: /health (sound_classification_enabled), /status (audio_scene)
Graceful degradation if model files not present.

Model download (not tracked in git):
  curl -sL 'https://tfhub.dev/google/lite-model/yamnet/classification/tflite/1?lite-format=tflite' -o models/yamnet.tflite

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.gitignore

@@ -2,6 +2,9 @@
 *.ppn
 Hey-Vivi_*/
 
+# ML models (downloaded separately)
+models/*.tflite
+
 # Python
 __pycache__/
 *.py[cod]

headmic.py

@@ -34,6 +34,7 @@ import wave
 from pathlib import Path
 from typing import Optional, List
 
+import numpy as np
 import httpx
 import pvporcupine
 import webrtcvad
@@ -117,9 +118,15 @@ class ServiceState:
         self.last_wake_time: Optional[float] = None
         self.wake_count = 0
         self.error: Optional[str] = None
+        self.audio_scene: Optional[dict] = None
+        self.sound_classification_enabled: bool = False
 
 state = ServiceState()
 
+# Sound classifier globals
+sound_classifier = None
+sound_ring_buffer = None  # collections.deque, filled by listener_loop
+
 
 # ============================================================================
 # Audio Stream using ALSA directly (arecord)
@@ -255,6 +262,10 @@ def listener_loop():
             # Convert bytes to int16 array for Porcupine
             pcm = struct.unpack_from("h" * 512, frame_data)
 
+            # Feed sound classifier ring buffer
+            if sound_ring_buffer is not None:
+                sound_ring_buffer.append(frame_data)
+
             # Check for wake word
             keyword_index = porcupine.process(pcm)
             
@@ -327,6 +338,31 @@ def listener_loop():
         logger.info("Listener stopped")
 
 
+# ============================================================================
+# Sound Classification Thread
+# ============================================================================
+
+def sound_classifier_loop():
+    """Background thread for continuous sound classification."""
+    global state
+    logger.info("Sound classifier thread started")
+    while state.running:
+        if sound_ring_buffer is None or len(sound_ring_buffer) < 30:
+            time.sleep(0.1)
+            continue
+
+        try:
+            frames = list(sound_ring_buffer)
+            audio = np.frombuffer(b"".join(frames), dtype=np.int16)
+            result = sound_classifier.classify(audio)
+            state.audio_scene = result
+        except Exception as e:
+            logger.warning("Sound classification error: %s", e)
+
+        time.sleep(0.5)
+    logger.info("Sound classifier thread stopped")
+
+
 # ============================================================================
 # FastAPI
 # ============================================================================
@@ -336,7 +372,30 @@ app = FastAPI(title="HeadMic", description="Vixy's Ears 🦊👂")
 
 @app.on_event("startup")
 async def startup():
+    global sound_classifier, sound_ring_buffer
+
     state.running = True
+
+    # Init sound classifier (optional — graceful if model missing)
+    model_dir = Path(__file__).parent / "models"
+    model_path = model_dir / "yamnet.tflite"
+    class_map_path = model_dir / "yamnet_class_map.csv"
+    if model_path.exists() and class_map_path.exists():
+        try:
+            from sound_id import SoundClassifier
+            sound_classifier = SoundClassifier(str(model_path), str(class_map_path))
+            # 31 frames of 512 samples = ~0.99s at 16kHz
+            sound_ring_buffer = collections.deque(maxlen=31)
+            state.sound_classification_enabled = True
+            logger.info("Sound classification enabled (YAMNet)")
+
+            sc_thread = threading.Thread(target=sound_classifier_loop, daemon=True)
+            sc_thread.start()
+        except Exception as e:
+            logger.warning("Sound classification unavailable: %s", e)
+    else:
+        logger.info("Sound classification models not found, skipping")
+
     thread = threading.Thread(target=listener_loop, daemon=True)
     thread.start()
     logger.info("HeadMic started")
@@ -365,6 +424,7 @@ async def health():
         "recording": state.recording,
         "processing": state.processing,
         "wake_count": state.wake_count,
+        "sound_classification_enabled": state.sound_classification_enabled,
         "error": state.error
     }
 
@@ -378,6 +438,7 @@ async def status():
         "last_transcription": state.last_transcription,
         "last_wake_time": state.last_wake_time,
         "wake_count": state.wake_count,
+        "audio_scene": state.audio_scene["dominant_category"] if state.audio_scene else None,
         "error": state.error
     }
 
@@ -390,6 +451,26 @@ async def last():
     }
 
 
+@app.get("/sounds")
+async def sounds():
+    """Current audio scene classification."""
+    if not state.sound_classification_enabled:
+        raise HTTPException(status_code=503, detail="Sound classification not available")
+    if state.audio_scene is None:
+        return {"category": None, "top_classes": [], "dominant_category": None, "timestamp": None}
+    return state.audio_scene
+
+
+@app.get("/sounds/history")
+async def sounds_history(seconds: int = 30):
+    """Recent sound classification history."""
+    if not state.sound_classification_enabled:
+        raise HTTPException(status_code=503, detail="Sound classification not available")
+    if sound_classifier is None:
+        return {"history": []}
+    return {"history": sound_classifier.get_history(seconds)}
+
+
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8446)

models/yamnet_class_map.csv

@@ -0,0 +1,522 @@
+index,mid,display_name
+0,/m/09x0r,Speech
+1,/m/0ytgt,"Child speech, kid speaking"
+2,/m/01h8n0,Conversation
+3,/m/02qldy,"Narration, monologue"
+4,/m/0261r1,Babbling
+5,/m/0brhx,Speech synthesizer
+6,/m/07p6fty,Shout
+7,/m/07q4ntr,Bellow
+8,/m/07rwj3x,Whoop
+9,/m/07sr1lc,Yell
+10,/t/dd00135,Children shouting
+11,/m/03qc9zr,Screaming
+12,/m/02rtxlg,Whispering
+13,/m/01j3sz,Laughter
+14,/t/dd00001,Baby laughter
+15,/m/07r660_,Giggle
+16,/m/07s04w4,Snicker
+17,/m/07sq110,Belly laugh
+18,/m/07rgt08,"Chuckle, chortle"
+19,/m/0463cq4,"Crying, sobbing"
+20,/t/dd00002,"Baby cry, infant cry"
+21,/m/07qz6j3,Whimper
+22,/m/07qw_06,"Wail, moan"
+23,/m/07plz5l,Sigh
+24,/m/015lz1,Singing
+25,/m/0l14jd,Choir
+26,/m/01swy6,Yodeling
+27,/m/02bk07,Chant
+28,/m/01c194,Mantra
+29,/t/dd00005,Child singing
+30,/t/dd00006,Synthetic singing
+31,/m/06bxc,Rapping
+32,/m/02fxyj,Humming
+33,/m/07s2xch,Groan
+34,/m/07r4k75,Grunt
+35,/m/01w250,Whistling
+36,/m/0lyf6,Breathing
+37,/m/07mzm6,Wheeze
+38,/m/01d3sd,Snoring
+39,/m/07s0dtb,Gasp
+40,/m/07pyy8b,Pant
+41,/m/07q0yl5,Snort
+42,/m/01b_21,Cough
+43,/m/0dl9sf8,Throat clearing
+44,/m/01hsr_,Sneeze
+45,/m/07ppn3j,Sniff
+46,/m/06h7j,Run
+47,/m/07qv_x_,Shuffle
+48,/m/07pbtc8,"Walk, footsteps"
+49,/m/03cczk,"Chewing, mastication"
+50,/m/07pdhp0,Biting
+51,/m/0939n_,Gargling
+52,/m/01g90h,Stomach rumble
+53,/m/03q5_w,"Burping, eructation"
+54,/m/02p3nc,Hiccup
+55,/m/02_nn,Fart
+56,/m/0k65p,Hands
+57,/m/025_jnm,Finger snapping
+58,/m/0l15bq,Clapping
+59,/m/01jg02,"Heart sounds, heartbeat"
+60,/m/01jg1z,Heart murmur
+61,/m/053hz1,Cheering
+62,/m/028ght,Applause
+63,/m/07rkbfh,Chatter
+64,/m/03qtwd,Crowd
+65,/m/07qfr4h,"Hubbub, speech noise, speech babble"
+66,/t/dd00013,Children playing
+67,/m/0jbk,Animal
+68,/m/068hy,"Domestic animals, pets"
+69,/m/0bt9lr,Dog
+70,/m/05tny_,Bark
+71,/m/07r_k2n,Yip
+72,/m/07qf0zm,Howl
+73,/m/07rc7d9,Bow-wow
+74,/m/0ghcn6,Growling
+75,/t/dd00136,Whimper (dog)
+76,/m/01yrx,Cat
+77,/m/02yds9,Purr
+78,/m/07qrkrw,Meow
+79,/m/07rjwbb,Hiss
+80,/m/07r81j2,Caterwaul
+81,/m/0ch8v,"Livestock, farm animals, working animals"
+82,/m/03k3r,Horse
+83,/m/07rv9rh,Clip-clop
+84,/m/07q5rw0,"Neigh, whinny"
+85,/m/01xq0k1,"Cattle, bovinae"
+86,/m/07rpkh9,Moo
+87,/m/0239kh,Cowbell
+88,/m/068zj,Pig
+89,/t/dd00018,Oink
+90,/m/03fwl,Goat
+91,/m/07q0h5t,Bleat
+92,/m/07bgp,Sheep
+93,/m/025rv6n,Fowl
+94,/m/09b5t,"Chicken, rooster"
+95,/m/07st89h,Cluck
+96,/m/07qn5dc,"Crowing, cock-a-doodle-doo"
+97,/m/01rd7k,Turkey
+98,/m/07svc2k,Gobble
+99,/m/09ddx,Duck
+100,/m/07qdb04,Quack
+101,/m/0dbvp,Goose
+102,/m/07qwf61,Honk
+103,/m/01280g,Wild animals
+104,/m/0cdnk,"Roaring cats (lions, tigers)"
+105,/m/04cvmfc,Roar
+106,/m/015p6,Bird
+107,/m/020bb7,"Bird vocalization, bird call, bird song"
+108,/m/07pggtn,"Chirp, tweet"
+109,/m/07sx8x_,Squawk
+110,/m/0h0rv,"Pigeon, dove"
+111,/m/07r_25d,Coo
+112,/m/04s8yn,Crow
+113,/m/07r5c2p,Caw
+114,/m/09d5_,Owl
+115,/m/07r_80w,Hoot
+116,/m/05_wcq,"Bird flight, flapping wings"
+117,/m/01z5f,"Canidae, dogs, wolves"
+118,/m/06hps,"Rodents, rats, mice"
+119,/m/04rmv,Mouse
+120,/m/07r4gkf,Patter
+121,/m/03vt0,Insect
+122,/m/09xqv,Cricket
+123,/m/09f96,Mosquito
+124,/m/0h2mp,"Fly, housefly"
+125,/m/07pjwq1,Buzz
+126,/m/01h3n,"Bee, wasp, etc."
+127,/m/09ld4,Frog
+128,/m/07st88b,Croak
+129,/m/078jl,Snake
+130,/m/07qn4z3,Rattle
+131,/m/032n05,Whale vocalization
+132,/m/04rlf,Music
+133,/m/04szw,Musical instrument
+134,/m/0fx80y,Plucked string instrument
+135,/m/0342h,Guitar
+136,/m/02sgy,Electric guitar
+137,/m/018vs,Bass guitar
+138,/m/042v_gx,Acoustic guitar
+139,/m/06w87,"Steel guitar, slide guitar"
+140,/m/01glhc,Tapping (guitar technique)
+141,/m/07s0s5r,Strum
+142,/m/018j2,Banjo
+143,/m/0jtg0,Sitar
+144,/m/04rzd,Mandolin
+145,/m/01bns_,Zither
+146,/m/07xzm,Ukulele
+147,/m/05148p4,Keyboard (musical)
+148,/m/05r5c,Piano
+149,/m/01s0ps,Electric piano
+150,/m/013y1f,Organ
+151,/m/03xq_f,Electronic organ
+152,/m/03gvt,Hammond organ
+153,/m/0l14qv,Synthesizer
+154,/m/01v1d8,Sampler
+155,/m/03q5t,Harpsichord
+156,/m/0l14md,Percussion
+157,/m/02hnl,Drum kit
+158,/m/0cfdd,Drum machine
+159,/m/026t6,Drum
+160,/m/06rvn,Snare drum
+161,/m/03t3fj,Rimshot
+162,/m/02k_mr,Drum roll
+163,/m/0bm02,Bass drum
+164,/m/011k_j,Timpani
+165,/m/01p970,Tabla
+166,/m/01qbl,Cymbal
+167,/m/03qtq,Hi-hat
+168,/m/01sm1g,Wood block
+169,/m/07brj,Tambourine
+170,/m/05r5wn,Rattle (instrument)
+171,/m/0xzly,Maraca
+172,/m/0mbct,Gong
+173,/m/016622,Tubular bells
+174,/m/0j45pbj,Mallet percussion
+175,/m/0dwsp,"Marimba, xylophone"
+176,/m/0dwtp,Glockenspiel
+177,/m/0dwt5,Vibraphone
+178,/m/0l156b,Steelpan
+179,/m/05pd6,Orchestra
+180,/m/01kcd,Brass instrument
+181,/m/0319l,French horn
+182,/m/07gql,Trumpet
+183,/m/07c6l,Trombone
+184,/m/0l14_3,Bowed string instrument
+185,/m/02qmj0d,String section
+186,/m/07y_7,"Violin, fiddle"
+187,/m/0d8_n,Pizzicato
+188,/m/01xqw,Cello
+189,/m/02fsn,Double bass
+190,/m/085jw,"Wind instrument, woodwind instrument"
+191,/m/0l14j_,Flute
+192,/m/06ncr,Saxophone
+193,/m/01wy6,Clarinet
+194,/m/03m5k,Harp
+195,/m/0395lw,Bell
+196,/m/03w41f,Church bell
+197,/m/027m70_,Jingle bell
+198,/m/0gy1t2s,Bicycle bell
+199,/m/07n_g,Tuning fork
+200,/m/0f8s22,Chime
+201,/m/026fgl,Wind chime
+202,/m/0150b9,Change ringing (campanology)
+203,/m/03qjg,Harmonica
+204,/m/0mkg,Accordion
+205,/m/0192l,Bagpipes
+206,/m/02bxd,Didgeridoo
+207,/m/0l14l2,Shofar
+208,/m/07kc_,Theremin
+209,/m/0l14t7,Singing bowl
+210,/m/01hgjl,Scratching (performance technique)
+211,/m/064t9,Pop music
+212,/m/0glt670,Hip hop music
+213,/m/02cz_7,Beatboxing
+214,/m/06by7,Rock music
+215,/m/03lty,Heavy metal
+216,/m/05r6t,Punk rock
+217,/m/0dls3,Grunge
+218,/m/0dl5d,Progressive rock
+219,/m/07sbbz2,Rock and roll
+220,/m/05w3f,Psychedelic rock
+221,/m/06j6l,Rhythm and blues
+222,/m/0gywn,Soul music
+223,/m/06cqb,Reggae
+224,/m/01lyv,Country
+225,/m/015y_n,Swing music
+226,/m/0gg8l,Bluegrass
+227,/m/02x8m,Funk
+228,/m/02w4v,Folk music
+229,/m/06j64v,Middle Eastern music
+230,/m/03_d0,Jazz
+231,/m/026z9,Disco
+232,/m/0ggq0m,Classical music
+233,/m/05lls,Opera
+234,/m/02lkt,Electronic music
+235,/m/03mb9,House music
+236,/m/07gxw,Techno
+237,/m/07s72n,Dubstep
+238,/m/0283d,Drum and bass
+239,/m/0m0jc,Electronica
+240,/m/08cyft,Electronic dance music
+241,/m/0fd3y,Ambient music
+242,/m/07lnk,Trance music
+243,/m/0g293,Music of Latin America
+244,/m/0ln16,Salsa music
+245,/m/0326g,Flamenco
+246,/m/0155w,Blues
+247,/m/05fw6t,Music for children
+248,/m/02v2lh,New-age music
+249,/m/0y4f8,Vocal music
+250,/m/0z9c,A capella
+251,/m/0164x2,Music of Africa
+252,/m/0145m,Afrobeat
+253,/m/02mscn,Christian music
+254,/m/016cjb,Gospel music
+255,/m/028sqc,Music of Asia
+256,/m/015vgc,Carnatic music
+257,/m/0dq0md,Music of Bollywood
+258,/m/06rqw,Ska
+259,/m/02p0sh1,Traditional music
+260,/m/05rwpb,Independent music
+261,/m/074ft,Song
+262,/m/025td0t,Background music
+263,/m/02cjck,Theme music
+264,/m/03r5q_,Jingle (music)
+265,/m/0l14gg,Soundtrack music
+266,/m/07pkxdp,Lullaby
+267,/m/01z7dr,Video game music
+268,/m/0140xf,Christmas music
+269,/m/0ggx5q,Dance music
+270,/m/04wptg,Wedding music
+271,/t/dd00031,Happy music
+272,/t/dd00033,Sad music
+273,/t/dd00034,Tender music
+274,/t/dd00035,Exciting music
+275,/t/dd00036,Angry music
+276,/t/dd00037,Scary music
+277,/m/03m9d0z,Wind
+278,/m/09t49,Rustling leaves
+279,/t/dd00092,Wind noise (microphone)
+280,/m/0jb2l,Thunderstorm
+281,/m/0ngt1,Thunder
+282,/m/0838f,Water
+283,/m/06mb1,Rain
+284,/m/07r10fb,Raindrop
+285,/t/dd00038,Rain on surface
+286,/m/0j6m2,Stream
+287,/m/0j2kx,Waterfall
+288,/m/05kq4,Ocean
+289,/m/034srq,"Waves, surf"
+290,/m/06wzb,Steam
+291,/m/07swgks,Gurgling
+292,/m/02_41,Fire
+293,/m/07pzfmf,Crackle
+294,/m/07yv9,Vehicle
+295,/m/019jd,"Boat, Water vehicle"
+296,/m/0hsrw,"Sailboat, sailing ship"
+297,/m/056ks2,"Rowboat, canoe, kayak"
+298,/m/02rlv9,"Motorboat, speedboat"
+299,/m/06q74,Ship
+300,/m/012f08,Motor vehicle (road)
+301,/m/0k4j,Car
+302,/m/0912c9,"Vehicle horn, car horn, honking"
+303,/m/07qv_d5,Toot
+304,/m/02mfyn,Car alarm
+305,/m/04gxbd,"Power windows, electric windows"
+306,/m/07rknqz,Skidding
+307,/m/0h9mv,Tire squeal
+308,/t/dd00134,Car passing by
+309,/m/0ltv,"Race car, auto racing"
+310,/m/07r04,Truck
+311,/m/0gvgw0,Air brake
+312,/m/05x_td,"Air horn, truck horn"
+313,/m/02rhddq,Reversing beeps
+314,/m/03cl9h,"Ice cream truck, ice cream van"
+315,/m/01bjv,Bus
+316,/m/03j1ly,Emergency vehicle
+317,/m/04qvtq,Police car (siren)
+318,/m/012n7d,Ambulance (siren)
+319,/m/012ndj,"Fire engine, fire truck (siren)"
+320,/m/04_sv,Motorcycle
+321,/m/0btp2,"Traffic noise, roadway noise"
+322,/m/06d_3,Rail transport
+323,/m/07jdr,Train
+324,/m/04zmvq,Train whistle
+325,/m/0284vy3,Train horn
+326,/m/01g50p,"Railroad car, train wagon"
+327,/t/dd00048,Train wheels squealing
+328,/m/0195fx,"Subway, metro, underground"
+329,/m/0k5j,Aircraft
+330,/m/014yck,Aircraft engine
+331,/m/04229,Jet engine
+332,/m/02l6bg,"Propeller, airscrew"
+333,/m/09ct_,Helicopter
+334,/m/0cmf2,"Fixed-wing aircraft, airplane"
+335,/m/0199g,Bicycle
+336,/m/06_fw,Skateboard
+337,/m/02mk9,Engine
+338,/t/dd00065,Light engine (high frequency)
+339,/m/08j51y,"Dental drill, dentist's drill"
+340,/m/01yg9g,Lawn mower
+341,/m/01j4z9,Chainsaw
+342,/t/dd00066,Medium engine (mid frequency)
+343,/t/dd00067,Heavy engine (low frequency)
+344,/m/01h82_,Engine knocking
+345,/t/dd00130,Engine starting
+346,/m/07pb8fc,Idling
+347,/m/07q2z82,"Accelerating, revving, vroom"
+348,/m/02dgv,Door
+349,/m/03wwcy,Doorbell
+350,/m/07r67yg,Ding-dong
+351,/m/02y_763,Sliding door
+352,/m/07rjzl8,Slam
+353,/m/07r4wb8,Knock
+354,/m/07qcpgn,Tap
+355,/m/07q6cd_,Squeak
+356,/m/0642b4,Cupboard open or close
+357,/m/0fqfqc,Drawer open or close
+358,/m/04brg2,"Dishes, pots, and pans"
+359,/m/023pjk,"Cutlery, silverware"
+360,/m/07pn_8q,Chopping (food)
+361,/m/0dxrf,Frying (food)
+362,/m/0fx9l,Microwave oven
+363,/m/02pjr4,Blender
+364,/m/02jz0l,"Water tap, faucet"
+365,/m/0130jx,Sink (filling or washing)
+366,/m/03dnzn,Bathtub (filling or washing)
+367,/m/03wvsk,Hair dryer
+368,/m/01jt3m,Toilet flush
+369,/m/012xff,Toothbrush
+370,/m/04fgwm,Electric toothbrush
+371,/m/0d31p,Vacuum cleaner
+372,/m/01s0vc,Zipper (clothing)
+373,/m/03v3yw,Keys jangling
+374,/m/0242l,Coin (dropping)
+375,/m/01lsmm,Scissors
+376,/m/02g901,"Electric shaver, electric razor"
+377,/m/05rj2,Shuffling cards
+378,/m/0316dw,Typing
+379,/m/0c2wf,Typewriter
+380,/m/01m2v,Computer keyboard
+381,/m/081rb,Writing
+382,/m/07pp_mv,Alarm
+383,/m/07cx4,Telephone
+384,/m/07pp8cl,Telephone bell ringing
+385,/m/01hnzm,Ringtone
+386,/m/02c8p,"Telephone dialing, DTMF"
+387,/m/015jpf,Dial tone
+388,/m/01z47d,Busy signal
+389,/m/046dlr,Alarm clock
+390,/m/03kmc9,Siren
+391,/m/0dgbq,Civil defense siren
+392,/m/030rvx,Buzzer
+393,/m/01y3hg,"Smoke detector, smoke alarm"
+394,/m/0c3f7m,Fire alarm
+395,/m/04fq5q,Foghorn
+396,/m/0l156k,Whistle
+397,/m/06hck5,Steam whistle
+398,/t/dd00077,Mechanisms
+399,/m/02bm9n,"Ratchet, pawl"
+400,/m/01x3z,Clock
+401,/m/07qjznt,Tick
+402,/m/07qjznl,Tick-tock
+403,/m/0l7xg,Gears
+404,/m/05zc1,Pulleys
+405,/m/0llzx,Sewing machine
+406,/m/02x984l,Mechanical fan
+407,/m/025wky1,Air conditioning
+408,/m/024dl,Cash register
+409,/m/01m4t,Printer
+410,/m/0dv5r,Camera
+411,/m/07bjf,Single-lens reflex camera
+412,/m/07k1x,Tools
+413,/m/03l9g,Hammer
+414,/m/03p19w,Jackhammer
+415,/m/01b82r,Sawing
+416,/m/02p01q,Filing (rasp)
+417,/m/023vsd,Sanding
+418,/m/0_ksk,Power tool
+419,/m/01d380,Drill
+420,/m/014zdl,Explosion
+421,/m/032s66,"Gunshot, gunfire"
+422,/m/04zjc,Machine gun
+423,/m/02z32qm,Fusillade
+424,/m/0_1c,Artillery fire
+425,/m/073cg4,Cap gun
+426,/m/0g6b5,Fireworks
+427,/g/122z_qxw,Firecracker
+428,/m/07qsvvw,"Burst, pop"
+429,/m/07pxg6y,Eruption
+430,/m/07qqyl4,Boom
+431,/m/083vt,Wood
+432,/m/07pczhz,Chop
+433,/m/07pl1bw,Splinter
+434,/m/07qs1cx,Crack
+435,/m/039jq,Glass
+436,/m/07q7njn,"Chink, clink"
+437,/m/07rn7sz,Shatter
+438,/m/04k94,Liquid
+439,/m/07rrlb6,"Splash, splatter"
+440,/m/07p6mqd,Slosh
+441,/m/07qlwh6,Squish
+442,/m/07r5v4s,Drip
+443,/m/07prgkl,Pour
+444,/m/07pqc89,"Trickle, dribble"
+445,/t/dd00088,Gush
+446,/m/07p7b8y,Fill (with liquid)
+447,/m/07qlf79,Spray
+448,/m/07ptzwd,Pump (liquid)
+449,/m/07ptfmf,Stir
+450,/m/0dv3j,Boiling
+451,/m/0790c,Sonar
+452,/m/0dl83,Arrow
+453,/m/07rqsjt,"Whoosh, swoosh, swish"
+454,/m/07qnq_y,"Thump, thud"
+455,/m/07rrh0c,Thunk
+456,/m/0b_fwt,Electronic tuner
+457,/m/02rr_,Effects unit
+458,/m/07m2kt,Chorus effect
+459,/m/018w8,Basketball bounce
+460,/m/07pws3f,Bang
+461,/m/07ryjzk,"Slap, smack"
+462,/m/07rdhzs,"Whack, thwack"
+463,/m/07pjjrj,"Smash, crash"
+464,/m/07pc8lb,Breaking
+465,/m/07pqn27,Bouncing
+466,/m/07rbp7_,Whip
+467,/m/07pyf11,Flap
+468,/m/07qb_dv,Scratch
+469,/m/07qv4k0,Scrape
+470,/m/07pdjhy,Rub
+471,/m/07s8j8t,Roll
+472,/m/07plct2,Crushing
+473,/t/dd00112,"Crumpling, crinkling"
+474,/m/07qcx4z,Tearing
+475,/m/02fs_r,"Beep, bleep"
+476,/m/07qwdck,Ping
+477,/m/07phxs1,Ding
+478,/m/07rv4dm,Clang
+479,/m/07s02z0,Squeal
+480,/m/07qh7jl,Creak
+481,/m/07qwyj0,Rustle
+482,/m/07s34ls,Whir
+483,/m/07qmpdm,Clatter
+484,/m/07p9k1k,Sizzle
+485,/m/07qc9xj,Clicking
+486,/m/07rwm0c,Clickety-clack
+487,/m/07phhsh,Rumble
+488,/m/07qyrcz,Plop
+489,/m/07qfgpx,"Jingle, tinkle"
+490,/m/07rcgpl,Hum
+491,/m/07p78v5,Zing
+492,/t/dd00121,Boing
+493,/m/07s12q4,Crunch
+494,/m/028v0c,Silence
+495,/m/01v_m0,Sine wave
+496,/m/0b9m1,Harmonic
+497,/m/0hdsk,Chirp tone
+498,/m/0c1dj,Sound effect
+499,/m/07pt_g0,Pulse
+500,/t/dd00125,"Inside, small room"
+501,/t/dd00126,"Inside, large room or hall"
+502,/t/dd00127,"Inside, public space"
+503,/t/dd00128,"Outside, urban or manmade"
+504,/t/dd00129,"Outside, rural or natural"
+505,/m/01b9nn,Reverberation
+506,/m/01jnbd,Echo
+507,/m/096m7z,Noise
+508,/m/06_y0by,Environmental noise
+509,/m/07rgkc5,Static
+510,/m/06xkwv,Mains hum
+511,/m/0g12c5,Distortion
+512,/m/08p9q4,Sidetone
+513,/m/07szfh9,Cacophony
+514,/m/0chx_,White noise
+515,/m/0cj0r,Pink noise
+516,/m/07p_0gm,Throbbing
+517,/m/01jwx6,Vibration
+518,/m/07c52,Television
+519,/m/06bz3,Radio
+520,/m/07hvw1,Field recording

sound_id.py

@@ -0,0 +1,182 @@
+"""
+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]