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16,000 | yeah |
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16,000 | <LAUGH> |
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16,000 | you know to death that really did not do it |
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16,000 | umhum |
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16,000 | no i have a two through you know two through wedge i hit a two iron awful lot off now |
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16,000 | umhum |
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16,000 | right |
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16,000 | uh practically a suspended sentence i mean it was |
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16,000 | yeah |
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16,000 | then i had seen something on television |
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16,000 | oh yeah that is always a challenge so do you do my wife talks with other people |
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16,000 | the justice system works is they bend over backwards trying to protect the guilty so many things in their back pardon yes i know |
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16,000 | RIGHT |
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16,000 | hm |
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16,000 | yeah |
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16,000 | you know uh |
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16,000 | she uh uh always our house borders richland college i mean you walk out our back door and you are on the campus |
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16,000 | you know the individuals to start thinking about what they can do to help each other out instead of counting on government to do everything |
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16,000 | you probably do not follow that |
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16,000 | hum |
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16,000 | yeah |
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16,000 | and you do not have to worry about uh fertilizing which causes a lot of thatch and all that kind of stuff so there is more and more people too are aware of that up here |
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16,000 | super vga and yeah it is fun i do programming too my job is uh edp auditor and so i am into computers all the time |
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16,000 | but |
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16,000 | sorry we have kind of an open house and the sound really travels um <LAUGH> |
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16,000 | well what have you seen recently |
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16,000 | have not got so fortunate as to get into any of those companies yet |
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16,000 | um we took a golf clinic there was about i do not know twelve fifteen people in it |
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16,000 | it is uh it is really bad here uh for example the uh local high school uh they have already found two students with sawed off shotguns |
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16,000 | right |
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16,000 | lot of WORK |
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16,000 | right that is when you see everybody is when somebody dies it is awful <LAUGH> |
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16,000 | is even on tv you watch it for two minutes and think this is so ridiculously stupid <LAUGH> |
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16,000 | and uh a um a nineteen eleven which is fairly scarce |
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16,000 | oh yeah yeah yeah |
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16,000 | oh how fun oh that is fun |
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16,000 | right |
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16,000 | are you uh and that my walking was a fast walk uh to get the heart rate up to an aerobic level |
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16,000 | yeah right |
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16,000 | down right ridiculous and i know driving through pittsburgh you know we got lost DOWN THERE ONE TIME and we we are trying to come home and um |
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16,000 | yeah |
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16,000 | uh near here and uh so but the houses around here there is five five houses on my street for sale right now |
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16,000 | english comedies |
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16,000 | umhum |
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16,000 | wow uhhuh |
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16,000 | i do not know i |
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16,000 | i think a lot of the commentators on like the major networks like right it is kind of appropriate right now because of the election stuff going on but um it seems that um |
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16,000 | okay uh mandatory service yeah i do not think it is a good idea |
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16,000 | i have i have done several of them it is it is it is all right it is you know |
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16,000 | yes |
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16,000 | yes |
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16,000 | yeah |
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16,000 | oh my gosh |
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16,000 | YEAH I KNOW know then WHEN one of the doors fall off then it is time to get a new car YOU KNOW |
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16,000 | yeah sounds like you have a real good garden |
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16,000 | well i guess we are supposed to talk about |
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16,000 | all right |
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16,000 | and |
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16,000 | yeah well |
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16,000 | oh really |
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16,000 | uh and and find out every |
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16,000 | well at least you will get very good at it right |
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16,000 | and they did not get across the river there so they are the last and that was actually after the war was over but it was the last |
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16,000 | i like the music but i have been unable to do that because i hurt my foot about five years ago i broke my heel |
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16,000 | yeah |
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16,000 | right |
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16,000 | he said he would lived on a farm too long and that was too much like camping ANYWAY true camping |
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16,000 | no <LAUGH> my mom has a cat though that does that |
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16,000 | when i can roll that old beast of mine down there for a lot less than that |
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16,000 | it it was fascinating it really was i really enjoyed it and i think everyone else did otherwise we would not keep doing it every morning |
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16,000 | yeah |
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16,000 | uhhuh |
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16,000 | i do not and i have an older sister that loves running too and she runs all the time |
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16,000 | <LAUGH> |
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16,000 | uhhuh |
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16,000 | a chicken |
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16,000 | yeah uhhuh ME TOO i am not sure that i use them as often as i think that i will but i sure uh pay attention to them and cut them out |
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16,000 | ouch |
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16,000 | you know men and women alike they give them say three hundred bucks a month and they can buy their health insurance and their retirement and whatever but |
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16,000 | so uh just a just a change of pace |
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16,000 | umhum that is true |
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16,000 | right |
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16,000 | uhhuh |
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16,000 | five pounds or |
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16,000 | oh sure |
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16,000 | oh |
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16,000 | took that uh went to war with iraq i think we that was kind of a uh display of power |
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16,000 | i do not i guess i could i have never been to a to a major golf tournament i uh watch them on tv a lot |
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16,000 | right |
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16,000 | right what kind of car is it |
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16,000 | that that would be important for me that that there was not that it was optional that if if every person and i could pick a time i guess rather than worrying about whether it was one years or two years suppose it were to be uh eighteen months |
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16,000 | umhum |
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16,000 | yeah |
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16,000 | uhhuh |
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16,000 | i know i i had a college professor that went i went to school in in missouri that is where we are from and he went to dc on a for a a big conference he is a political science uh |
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16,000 | fortunately |
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16,000 | oh okay |
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16,000 | yeah |
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16,000 | it is really a sad situation |
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16,000 | but there there definitely needs to be a BALANCE SOMEWHERE |
End of preview. Expand
in Dataset Viewer.
A preprocessed version of Switchboard Corpus
. The corpus audio has been upsampled to 16kHz, separated channels and the transcripts have been processed
with special treats for paralinguistic events, particularly laughter and speech-laughs.
This preprocessed dataset has been processed for ASR task. For the original dataset, please check out the original link: https://catalog.ldc.upenn.edu/LDC97S62
The dataset has been splitted into train, test and validation sets with 70/20/10 ratio, as following summary:
Train Dataset (70%): Dataset({
features: ['audio', 'sampling_rate', 'transcript'],
num_rows: 185402
})
Validation Dataset (10%): Dataset({
features: ['audio', 'sampling_rate', 'transcript'],
num_rows: 20601
})
Test Dataset (20%): Dataset({
features: ['audio', 'sampling_rate', 'transcript'],
num_rows: 51501
})
An example of the content is this dataset:
Regarding the total amount of laughter and speech-laugh existing in the dataset, here is the overview:
Train Dataset (swb_train): {'laughter': 16044, 'speechlaugh': 9586}
Validation Dataset (swb_val): {'laughter': 1845, 'speechlaugh': 1133}
Test Dataset (swb_test): {'laughter': 4335, 'speechlaugh': 2775}
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