Datasets:
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sampling_rate
dtype: int64
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 26537763371.78
num_examples: 185402
- name: validation
num_bytes: 2948998696.305
num_examples: 20601
- name: test
num_bytes: 7390220553.37
num_examples: 51501
download_size: 29378895903
dataset_size: 36876982621.455
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
task_categories:
- automatic-speech-recognition
tags:
- paralinguistic
- laughter
pretty_name: switchboard-speechlaugh
size_categories:
- 100K<n<1M
Corpus Overview
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 for contributed original authors.
To download the original dataset, it can be found at:
https://drive.google.com/drive/folders/1YhpWgzCwc4cVhYJPcjuLWy84s-L0hJbf
or using gdown:
gdown 1YhpWgzCwc4cVhYJPcjuLWy84s-L0hJbf -O /path/to/dataset/switchboard
Corpus Structure
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:
Specifications
Regarding the total amount of laughter and speech-laugh existing in the dataset, which using for specific task for Laughter and Speech-laugh Recognition, here is the additional 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}