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Datasets:
yangwang825
/
audioset

Tasks:
Audio Classification
Modalities:
Text
Formats:
json
Size:
1M - 10M
Tags:
audioset
Libraries:
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pandas
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filename
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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" ]
End of preview. Expand in Dataset Viewer.

AudioSet

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].

References

  1. Gemmeke, Jort F., et al., Audio set: An ontology and human-labeled dataset for audio events, 2017
  2. Kong, Qiuqiang, et al., Panns: Large-scale pretrained audio neural networks for audio pattern recognition, 2020
  3. Nagrani, Arsha, et al., Attention bottlenecks for multimodal fusion, 2021
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
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Number of rows:
2,460,565

Models trained or fine-tuned on yangwang825/audioset