attentionisallyouneed369/ssamba
Audio Classification
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Updated
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1
filename
stringlengths 12
12
| label
sequence |
---|---|
Y---1_cCGK4M | [
"Railroad car, train wagon",
"Train horn",
"Rail transport",
"Train",
"Clickety-clack"
] |
Y---EDNidJUA | [
"Narration, monologue",
"Female speech, woman speaking",
"Male speech, man speaking",
"Speech"
] |
Y---lTs1dxhU | [
"Motor vehicle (road)",
"Vehicle",
"Car",
"Car passing by"
] |
Y---yQzzLcFU | [
"Heavy engine (low frequency)"
] |
Y--04kMEQOAs | [
"Run",
"Speech"
] |
Y--0PQM4-hqg | [
"Gurgling",
"Waterfall",
"Stream"
] |
Y--0vTxLiRuQ | [
"Music",
"Roll"
] |
Y--11PIhoFjg | [
"Clatter"
] |
Y--1XHaNcX2Y | [
"Singing",
"Music of Africa",
"Music"
] |
Y--299m5_DdE | [
"Gurgling",
"Waterfall"
] |
Y--2XRMjyizo | [
"Bird",
"Bird vocalization, bird call, bird song",
"Chirp, tweet"
] |
Y--2iIT25cNE | [
"Music",
"Musical instrument",
"Drum machine"
] |
Y--2zH6Gmu0Q | [
"Sliding door"
] |
Y--330hg-Ocw | [
"Engine",
"Vehicle",
"Car",
"Medium engine (mid frequency)",
"Engine starting"
] |
Y--34LejG4cE | [
"Brass instrument",
"Trombone"
] |
Y--385LpykT0 | [
"Electronic music",
"Music",
"Ambient music",
"Synthesizer"
] |
Y--3flh9REUI | [
"Music",
"Tender music"
] |
Y--3jW_uh2Pk | [
"Music",
"Ambient music"
] |
Y--42a6bv16w | [
"Narration, monologue",
"Music",
"Male speech, man speaking",
"Speech"
] |
Y--46xGNV1H0 | [
"Heavy engine (low frequency)"
] |
Y--4kp9W7cNY | [
"Singing",
"Reggae"
] |
Y--51d28O-tM | [
"Speech",
"Male singing"
] |
Y--5A5ZCa1dE | [
"Vehicle",
"Fixed-wing aircraft, airplane",
"Aircraft"
] |
Y--5OkAjCI7g | [
"Belly laugh",
"Child speech, kid speaking"
] |
Y--79SFzTl1Y | [
"Speech"
] |
Y--7MeTMkd4s | [
"Music",
"Independent music"
] |
Y--7m0TsA030 | [
"Electric guitar",
"Guitar",
"Acoustic guitar",
"Music",
"Musical instrument",
"Strum"
] |
Y--7srtCMEQQ | [
"Ship",
"Vehicle"
] |
Y--8P9gLvO0Q | [
"Wood block",
"Music"
] |
Y--8hipdKBT4 | [
"Motor vehicle (road)",
"Vehicle",
"Speech",
"Car"
] |
Y--8puiAGLhs | [
"Engine",
"Vehicle",
"Car",
"Engine starting"
] |
Y--9O4XZOge4 | [
"Narration, monologue",
"Female speech, woman speaking",
"Speech"
] |
Y--9VR_F7CtY | [
"Motor vehicle (road)",
"Skidding",
"Vehicle",
"Car"
] |
Y--9hKb7IkVY | [
"Heavy metal",
"Music"
] |
Y--9oYufMS_k | [
"Music",
"Gasp"
] |
Y--A4Xbd8gCw | [
"Bass guitar",
"Guitar",
"Music",
"Musical instrument",
"Plucked string instrument",
"Salsa music"
] |
Y--AQYzDx57k | [
"Chuckle, chortle",
"Speech"
] |
Y--Aig9EHjy0 | [
"Wind",
"Wind noise (microphone)"
] |
Y--BB-7-YoIk | [
"Singing",
"Music"
] |
Y--BCZB_m2q0 | [
"Basketball bounce",
"Music",
"Sound effect"
] |
Y--BFPeFaj2o | [
"Railroad car, train wagon",
"Rail transport",
"Train",
"Vehicle",
"Outside, rural or natural"
] |
Y--BIwg9KRxI | [
"Radio",
"Speech",
"Electronic tuner"
] |
Y--BdguqnSjY | [
"Reverberation",
"Music",
"Speech"
] |
Y--BslWBgH3k | [
"Background music",
"Music",
"Speech"
] |
Y--Bu2xe4OSo | [
"Boat, Water vehicle",
"Wind",
"Rustle",
"Vehicle",
"Speech",
"Wind noise (microphone)",
"Outside, rural or natural"
] |
Y--C2fgwf0vg | [
"Sizzle"
] |
Y--CE2f-ttEQ | [
"Music",
"Dog"
] |
Y--CHY2qO5zc | [
"Tick-tock",
"Tick"
] |
Y--CZ-8vrQ1g | [
"Music",
"Happy music"
] |
Y--Cc0ZmStCs | [
"Music",
"Rhythm and blues",
"Dance music"
] |
Y--DPrkc66qI | [
"Speech synthesizer"
] |
Y--E3k28veVc | [
"Music",
"Classical music"
] |
Y--EG-JqO4S0 | [
"Engine",
"Idling",
"Accelerating, revving, vroom",
"Speech",
"Engine starting"
] |
Y--EQegXxPiI | [
"Music",
"Orchestra",
"Classical music"
] |
Y--ERHDSdxGQ | [
"Music",
"Domestic animals, pets",
"Bow-wow",
"Speech",
"Dog",
"Animal"
] |
Y--ERXu9VVGE | [
"Video game music",
"Music"
] |
Y--EnKcYsPas | [
"Babbling"
] |
Y--Evvi58CcI | [
"Traditional music",
"Music"
] |
Y--Fti-jdXEI | [
"Music",
"Vocal music"
] |
Y--G-wKyj6JQ | [
"Harpsichord",
"Music"
] |
Y--GXulx19TI | [
"Music",
"Pop music"
] |
Y--GY4nqPqOI | [
"Writing"
] |
Y--GcThRqfjM | [
"Music",
"Female singing"
] |
Y--Gy6Dsgf1A | [
"Speech",
"Stream"
] |
Y--HXYSM3ydo | [
"Vehicle horn, car horn, honking",
"Speech"
] |
Y--HiqZbHZUE | [
"Run"
] |
Y--INXrf9zV4 | [
"Music",
"Soul music"
] |
Y--IVng5n_Mw | [
"Horse",
"Neigh, whinny",
"Animal"
] |
Y--IsizwatBY | [
"Music",
"Rhythm and blues"
] |
Y--ItuZWjmtE | [
"Crow"
] |
Y--J1326hTc0 | [
"Video game music",
"Jingle (music)",
"Music"
] |
Y--Jcz_RawUA | [
"Chainsaw",
"Lawn mower"
] |
Y--JxAySnD3Y | [
"Zither",
"Guitar",
"Acoustic guitar",
"Musical instrument",
"Plucked string instrument"
] |
Y--K3100xfu8 | [
"Music",
"Sad music"
] |
Y--K53tRgAOg | [
"Mechanical fan"
] |
Y--K91QrLI4g | [
"Female speech, woman speaking",
"Speech"
] |
Y--KCIeTv6PM | [
"Cat",
"Domestic animals, pets",
"Caterwaul",
"Animal"
] |
Y--KWWlNH1O0 | [
"Jingle (music)",
"Music",
"Music of Latin America"
] |
Y--KdMg39p4k | [
"Music",
"Sonar"
] |
Y--KjQn5OdHA | [
"Vibration",
"Speech"
] |
Y--L22BmDI6E | [
"Domestic animals, pets",
"Yip",
"Dog",
"Animal",
"Whimper (dog)"
] |
Y--L3BCCcGEw | [
"Applause",
"Speech"
] |
Y--L9-DQKtlk | [
"Water tap, faucet",
"Speech",
"Inside, small room"
] |
Y--LGPn-g2R4 | [
"Music",
"Sound effect"
] |
Y--LGvpWGBAI | [
"Music",
"Cheering",
"Inside, public space"
] |
Y--Lj4Y_96f0 | [
"Bee, wasp, etc.",
"Insect",
"Fly, housefly"
] |
Y--LxRKErLk8 | [
"Jingle (music)",
"Music"
] |
Y--MTT7hiVV0 | [
"Reverberation",
"Effects unit",
"Music"
] |
Y--N8lbFywRg | [
"Stream"
] |
Y--NDLv9k8PY | [
"Marimba, xylophone",
"Glockenspiel",
"Mallet percussion"
] |
Y--Nk4m6mvHc | [
"Guitar",
"Acoustic guitar",
"Music",
"Musical instrument"
] |
Y--OMDPXfO6o | [
"Music",
"Speech",
"Fire alarm"
] |
Y--OWy19KnMI | [
"Hammer"
] |
Y--OewGtwfTs | [
"Music",
"Rattle",
"Speech"
] |
Y--P4wuph3Mc | [
"Motor vehicle (road)",
"Vehicle",
"Speech",
"Car",
"Car passing by"
] |
Y--PG66A3lo4 | [
"Gunshot, gunfire",
"Machine gun"
] |
Y--PLvH-OZRI | [
"Music",
"Dance music",
"Exciting music"
] |
Y--PVtGGYpKY | [
"Whispering",
"Speech"
] |
Y--Pdds1vgbM | [
"Firecracker"
] |
Y--PpjbpSCe8 | [
"Music",
"Radio",
"Speech"
] |
AudioSet[1] consists of an expanding ontology of 527 audio event classes and a collection of 2M human-labelled 10-second sound clips drawn from YouTube. Some clips are missing on YouTube, so the number of files downloaded is different from time to time. This repository contains 20550 / 22160 of the balanced train set, 1913637 / 2041789 of the unbalanced train set (separated into 41 parts), and 18887 / 20371 of the evaluation set. The pre-process script can be found at qiuqiangkong's github[2].
To improve training efficiency, we add a slightly more balanced subset AudioSet500K[3].