README.md

---
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} 
```