Datasets:
File size: 1,996 Bytes
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---
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
pretty_name: a
size_categories:
- 100K<n<1M
---
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:
```python
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:
```bash
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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