Datasets:
meeting_id
stringclasses 32
values | audio_id
stringlengths 38
38
| text
stringlengths 1
358
| audio
audioduration (s) 0.02
25.3
| begin_time
float32 0
5.3k
| end_time
float32 0.34
5.3k
| microphone_id
stringclasses 5
values | speaker_id
stringclasses 43
values |
---|---|---|---|---|---|---|---|
EN2001a | AMI_EN2001a_H04_MEO069_0330297_0330718 | IF YOU IF YOU S. S. H. AND THEY HAVE THIS BIG WARNING ABOUT DOING NOTHING AT ALL IN THE GATEWAY MACHINE | 3,302.97 | 3,307.18 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0414915_0415078 | I'VE GOTTEN MM HARDLY ANY | 4,149.15 | 4,150.78 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0319290_0319815 | IT'S YEAH I MEAN THE WAVE DATA ARE OBVIOUSLY NOT GONNA GET OFF THERE COMPLETELY | 3,192.9 | 3,198.15 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0145515_0146152 | YEAH IT'LL IT'LL PLAY THEM IN SOME ORDER IN WHICH THEY WERE SET BECAUSE OTHERWISE IT'S GONNA BE MORE ENTERTAINING | 1,455.15 | 1,461.52 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0478127_0478164 | YEAH | 4,781.27 | 4,781.64 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H02_FEO065_0436920_0436957 | HMM | 4,369.2 | 4,369.57 | H02 | FEO065 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0171941_0172087 | ALL THESE FANCY PENS | 1,719.41 | 1,720.87 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0122764_0123754 | LIKE SOMETHING THAT REPRESENTS THE WHOLE SERIES IN IN A V IN A STRUCTURE VERY SIMILAR TO THE STRUCTURE IN WHICH WE REPRESENT INDIVIDUAL UM MEETINGS | 1,227.64 | 1,237.54 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0368111_0368920 | 'CAUSE IF WE'RE GONNA ALLOW DISJOINT SEGMENTS FOR EXAMPLE THEN HOW ARE WE GONNA KNOW WHAT'S GONNA BE IN CONTEXT AT ANY GIVEN TIME | 3,681.11 | 3,689.2 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0292554_0293396 | IS ANYONE OF YOU FOR THE FOR THE DOCUMENT FREQUENCY OVER TOTAL FREQUENCY YOU GONNA HAVE TOTAL FREQUENCIES OF WORDS THEN WITH THAT RIGHT | 2,925.54 | 2,933.96 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0296353_0296603 | LIKE I DON'T KNOW COPIES OF SHAKESPEARE OR SOMETHING | 2,963.53 | 2,966.03 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H02_FEO065_0081159_0081631 | I'M NOT QUITE SO WHAT IT DID YOU WANT TO DO IT I YOU JUST WANTED TO ASSIGN | 811.59 | 816.31 | H02 | FEO065 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0210498_0210848 | AND THAT WILL OBVIOUSLY MAKE IT MUCH EASIER TO DISPLAY | 2,104.98 | 2,108.48 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0436592_0437029 | UH I ORDERED ACCORDING TO THE UM STARTING TIMES OF THE UTTERANCES | 4,365.92 | 4,370.29 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0032020_0032405 | 'CAUSE IF WE ARE I RECKON WE SHOULD ALL READ OUR CLASSES OUT OF THE DATABASE | 320.2 | 324.05 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0143300_0143643 | AND AND PROBABLY SEPARATE TO THAT AN INFORMATION ABOUT THE DIFFERENT TOPICS LIKE THAT | 1,433 | 1,436.43 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0043779_0044242 | THAT MEANS SORT OF WE HAVE MULTIPLE LEVELS OF OF REPRESENTATION WHICH WE PROBABLY | 437.79 | 442.42 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0276847_0276933 | HMM | 2,768.47 | 2,769.33 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0189116_0189239 | YEAH | 1,891.16 | 1,892.39 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0044746_0044987 | THEN WE SHOULD PROBABLY FIND SOME ABSTRACTION MODEL | 447.46 | 449.87 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0031358_0031560 | ARE WE STILL GONNA DUMP IT INTO A DATABASE | 313.58 | 315.6 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0405743_0405802 | SAY | 4,057.43 | 4,058.02 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0511004_0511535 | LIKE WITH THE DATA STRUCTURES I'M JUST LIKE OVER THESE VAGUE IDEAS OF SOME TREES I'M F | 5,110.04 | 5,115.35 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0240098_0240136 | YEAH YEAH | 2,400.98 | 2,401.36 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0194411_0194948 | I THOUGHT THAT WAS THE WHOLE BEAUTY THAT LIKE YOU CAN JUST MAKE A NEW X. M. L. FILE AND SORT OF TIE THAT TO THE OTHER AND AND IT TRE | 1,944.11 | 1,949.48 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0205991_0206009 | HMM | 2,059.91 | 2,060.09 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0119247_0119290 | HMM | 1,192.47 | 1,192.9 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0132274_0132288 | MM | 1,322.74 | 1,322.88 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0007991_0008261 | THE THING IS I'M AWAY THIS WEEKEND | 79.91 | 82.61 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0380400_0381446 | SO I'M JUST WONDERING IF THERE'S WAYS TO ABANDON THE WHOLE CONCEPT OF OF MEETINGS AND SORT OF BUT JUST NOT REALLY TREATING SEPARATE MEETINGS AS TOO MUCH OF A SEPARATE ENTITY | 3,804 | 3,814.46 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0495951_0496174 | SO I'D JUST BE BUILDING THE DATA STRUCTURE AGAIN | 4,959.51 | 4,961.74 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0342264_0342453 | YEAH YOU'D HAVE TO COUNT IT YOURSELF YEAH | 3,422.64 | 3,424.53 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0304005_0304468 | THEN SKIP IT BECAUSE IT'S PROBABLY SOMETHING WITH A DOT IN BETWEEN WHICH IS USUALLY NOT SOMETHING YOU WANNA HAVE AND | 3,040.05 | 3,044.68 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0325487_0325565 | THE TEMPS YEAH | 3,254.87 | 3,255.65 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0275384_0275512 | ARE THEY SPOKEN NUMBERS | 2,753.84 | 2,755.12 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0176892_0176988 | WELL THAT'S EASY | 1,768.92 | 1,769.88 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0474962_0475221 | THAT'S WHAT I'M GUESSING THAT'S YOU KNOW | 4,749.62 | 4,752.21 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0120444_0120583 | LET'S CHECK THAT OUT | 1,204.44 | 1,205.83 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0012177_0012665 | AND THEN JUST SORT OF EVERYONE MAKE SURE EVERYONE UNDERSTAND THE INTERFACE | 121.77 | 126.65 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0484951_0484980 | YEAH | 4,849.51 | 4,849.8 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0097639_0097766 | IN MEMORY YEAH | 976.39 | 977.66 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0194391_0194411 | YEAH | 1,943.91 | 1,944.11 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0212730_0213069 | YEAH AND THAT'S ALSO FAIRLY EASY TO STORE ALONG WITH OUR SEGMENTS ISN'T IT | 2,127.3 | 2,130.69 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0099586_0099696 | OKAY | 995.86 | 996.96 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0441100_0441243 | YEAH ONE GROUP YEAH | 4,411 | 4,412.43 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0503539_0503807 | WHEN DO WE HAVE TO MEET AGAIN THEN WITH THIS | 5,035.39 | 5,038.07 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0309109_0309372 | YEAH I IT WOULD BE USEFUL FOR ME AS WELL | 3,091.09 | 3,093.72 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0000560_0000601 | GOSH | 5.6 | 6.01 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0195726_0196470 | SORT OF C I WAS JUST THINKING YOU KNOW LIKE IF IF THE OVERHEAD FOR HAVING THE SAME AMOUNT OF DATA COMING FROM TWO D FILES INSTEAD OF FROM ONE FILE IS MASSIVE | 1,957.26 | 1,964.7 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0309421_0309606 | AM I THE ONLY ONE WHO NEEDS IT WITH FREQUENCIES | 3,094.21 | 3,096.06 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0333857_0334962 | UM I JUST UM WONDERED SO WHO'S UH THEN DOING UM THE FREQUENCIES ON ON THE WORDS | 3,338.57 | 3,349.62 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0323471_0323505 | YEAH | 3,234.71 | 3,235.05 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0463820_0464033 | YEAH FOR ME IT'S BETTER IF THEY'RE BY MEETING | 4,638.2 | 4,640.33 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0025836_0026585 | TH YEAH THE SEARCH IS I GUESS THE SEARCH IS SORT OF A STRANGE BEAST ANYWAY BECAUSE FOR THE SEARCH WE'RE LEAVING THE NITE X. M. L. FRAMEWORK | 258.36 | 265.85 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0466281_0466707 | YEAH ONE SERIES HAS THE UM SAME THREE STARTING LETTERS | 4,662.81 | 4,667.07 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0098250_0098399 | AND JUST BUILD ONE IN MEMORY | 982.5 | 983.99 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0098777_0098944 | I HAVE NO IDEA | 987.77 | 989.44 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0424718_0425031 | I HAVE THAT REALLY EXCITED PIRATE COPIED THING | 4,247.18 | 4,250.31 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0023174_0023651 | SO BASICALLY APART FROM THE DISPLAY MODULE THE I THE DISPLAY ITSELF | 231.74 | 236.51 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H02_FEO065_0490168_0490205 | YEAH | 4,901.68 | 4,902.05 | H02 | FEO065 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0162752_0162930 | N UH NO NO IT'S F FOR | 1,627.52 | 1,629.3 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0087994_0088294 | NO BUT I MEAN LIKE HOW HOW JASMINE DOES IT INTERNALLY I DON'T KNOW | 879.94 | 882.94 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0433402_0433692 | I CAN TRY TO DO IT AND SEND IT TO YOU | 4,334.02 | 4,336.92 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0181418_0181435 | RIGHT | 1,814.18 | 1,814.35 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H02_FEO065_0435874_0436021 | DID YOU ALSO ORDER | 4,358.74 | 4,360.21 | H02 | FEO065 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0093349_0093712 | SORT OF LIKE BUT THAT LIKE THE PROBLEM WITH THAT IS IT'S EASY TO DO IN THE TEXT LEVEL | 933.49 | 937.12 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0454853_0454997 | LIKE IS IT JUST THE FIRST AND THE LAST LINE | 4,548.53 | 4,549.97 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0024471_0025049 | SO THE INTERFACE IS MAINLY WHILE IT'S RUNNING JUST WORKING ON DATA THAT'S JUST LOADED FROM A FILE I GUESS | 244.71 | 250.49 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0289725_0290180 | IT'S JUST LIKE BEF UNTIL THE INFORMATION DENSITY IS UP AND RUNNING | 2,897.25 | 2,901.8 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0405802_0406582 | SO THEN YOU'D START WITH ALL YOUR UTTERANCES HERE AND WHEN YOU GO UP TO GET TOPIC SEGMENTS YOU GO TO HERE HERE HERE HERE HERE HERE HERE | 4,058.02 | 4,065.82 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0372047_0372898 | BECAUSE IF WE'RE DOING LIKE I THINK FOR FOR THE INFORMATION DENSITY WE UH WE SHOULD CALCULATE IT ON THE LOWEST LEVEL NOT ON THE HIGHEST | 3,720.47 | 3,728.98 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0144018_0144100 | SO | 1,440.18 | 1,441 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0141169_0141314 | AND THEY HAVE A SCORE | 1,411.69 | 1,413.14 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0060512_0060622 | MM-HMM | 605.12 | 606.22 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H02_FEO065_0271867_0272305 | YES BUT WHAT ARE THE OTHER THINGS THAT'S UH SOME KIND OF NUMBER | 2,718.67 | 2,723.05 | H02 | FEO065 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0064517_0065545 | AND THAT'S NOT SO MUCH WHAT HE MEANT WITH NOT POSSIBLY LOADING EVERYTHING WAS THAT YOU M UM LOAD ALL THE UH ANNOTATION STUFF | 645.17 | 655.45 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0070589_0070782 | FOR EVERY SINGLE WORD | 705.89 | 707.82 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0149028_0149049 | HMM | 1,490.28 | 1,490.49 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0217655_0217714 | YEAH | 2,176.55 | 2,177.14 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0372898_0372951 | BUT LIKE 'CAUSE | 3,728.98 | 3,729.51 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0188191_0188384 | FOR EXAMPLE FOR THE DIALOGUE ACTS AND SO ON | 1,881.91 | 1,883.84 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0047482_0047500 | HMM | 474.82 | 475 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0138344_0139072 | OKAY SO MAYBE WE SHOULD BUILD A B STORE A MEAN MEASURE FOR THE SEGMENTS AND MEETINGS AS WELL | 1,383.44 | 1,390.72 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0139834_0140209 | THEN MAYBE WE CAN MORE OR LESS USE THE SAME CODE AND JUST MAKE A FEW IFS AND STUFF | 1,398.34 | 1,402.09 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0489772_0489847 | HOW DO YOU DO THAT | 4,897.72 | 4,898.47 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0108365_0109244 | BUT I'M I'M STILL CONFUSED 'CAUSE I THOUGHT LIKE THAT'S JUST WHAT JONATHAN SAID WE DO C THAT WE CAN'T DO LIKE LOAD A MASSIVE DOCUMENT OF THAT SIZE | 1,083.65 | 1,092.44 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0458471_0458693 | OH THEN I NEED SOMETHING DIFFERENT LATER ANYWAY | 4,584.71 | 4,586.93 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0353711_0353909 | YEAH I I NEED FREQUENCY AS WELL | 3,537.11 | 3,539.09 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0310337_0310812 | WE CAN PROBABLY JUST START WITH THE JAVA HASH MAP AND LIKE JUST HASH MAP OVER IT AND SEE HOW FAR WE GET | 3,103.37 | 3,108.12 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H02_FEO065_0263972_0264145 | AND THEN YEAH | 2,639.72 | 2,641.45 | H02 | FEO065 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0504724_0504888 | DO WE HAVE TO DEMONSTRATE SOMETHING NEXT WEEK | 5,047.24 | 5,048.88 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0333444_0333587 | UH TH YEAH | 3,334.44 | 3,335.87 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0412181_0412299 | YEAH | 4,121.81 | 4,122.99 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0339398_0339933 | I CAN PROBABLY JUST IMPLEMENT LIKE A FIVE LINE JAVA HASH TABLE FREQUENCY DICTIONARY BUILDER AND SEE | 3,393.98 | 3,399.33 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0445600_0445650 | OKAY | 4,456 | 4,456.5 | H04 | MEO069 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0333826_0333857 | 'KAY | 3,338.26 | 3,338.57 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H01_FEO066_0337667_0338486 | SO UM I WOULD FOR EXAMPLE NEED THE UM MOST FREQ UM FREQUENT WORDS | 3,376.67 | 3,384.86 | H01 | FEO066 |
|
EN2001a | AMI_EN2001a_H03_MEE067_0232846_0232936 | LIKE AFTER THIS | 2,328.46 | 2,329.36 | H03 | MEE067 |
|
EN2001a | AMI_EN2001a_H00_MEE068_0026583_0026620 | YEAH | 265.83 | 266.2 | H00 | MEE068 |
|
EN2001a | AMI_EN2001a_H04_MEO069_0053867_0054209 | AND JUST HAVE DIFFERENT LIKE FINE GRAINEDNESS LEVELS SORT OF | 538.67 | 542.09 | H04 | MEO069 |
Dataset Card for AMI
Dataset Description
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers.
Note: This dataset corresponds to the data-processing of KALDI's AMI S5 recipe. This means text is normalized and the audio data is chunked according to the scripts above! To make the user experience as simply as possible, we provide the already chunked data to the user here so that the following can be done:
Example Usage
from datasets import load_dataset
ds = load_dataset("edinburghcstr/ami", "ihm")
print(ds)
gives:
DatasetDict({
train: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 108502
})
validation: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 13098
})
test: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 12643
})
})
ds["train"][0]
automatically loads the audio into memory:
{'meeting_id': 'EN2001a',
'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
'text': 'OKAY',
'audio': {'path': '/cache/dir/path/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav',
'array': array([0. , 0. , 0. , ..., 0.00033569, 0.00030518,
0.00030518], dtype=float32),
'sampling_rate': 16000},
'begin_time': 5.570000171661377,
'end_time': 5.940000057220459,
'microphone_id': 'H00',
'speaker_id': 'MEE068'}
The dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly the wav2vec2-large-lv60
checkpoint.
As can be seen in this experiments, training the model for less than 2 epochs gives
Result (WER):
"dev" | "eval" |
---|---|
25.27 | 25.21 |
as can be seen here.
The results are in-line with results of published papers:
- Hybrid acoustic models for distant and multichannel large vocabulary speech recognition
- Multi-Span Acoustic Modelling using Raw Waveform Signals
You can run run.sh to reproduce the result.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
Data Fields
Data Splits
Transcribed Subsets Size
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
Contributions
Thanks to @sanchit-gandhi, @patrickvonplaten, and @polinaeterna for adding this dataset.
Terms of Usage
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