Datasets:
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---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
languages:
- en
- zh-CN
licenses:
- cc-by-sa-4.0
multilinguality:
- multilingual
pretty_name: 'ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in
Multi-turn Conversation'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids:
- code-switching
- speech-recognition
---
# Dataset Card for ASCEND
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Usage](#usage)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/2112.06223
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.
### Supported Tasks and Leaderboards
Code-switching.
### Languages
Chinese and English
## Usage
```
import datasets
dataset = datasets.load_dataset("CAiRE/ASCEND", "train") # split: "train", "validation", "test"
```
## Dataset Structure
A typical data point comprises the path to the audio file, the loaded audio array, and its transcription. Additional fields include datapoint id, duration, language, speaker id, session id, and topic.
```
{
'id': '00000',
'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses1_spk17_L3818_9.3200_0.6400.wav',
'audio': {
'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses1_spk17_L3818_9.3200_0.6400.wav',
'array': array([0.00057983, 0.00073242, 0.00125122, ..., 0.00204468, 0.00250244,
0.00201416
], dtype = float32),
'sampling_rate': 16000
},
'transcription': '好的',
'duration': 0.6399999856948853,
'language': 'zh',
'original_speaker_id': 17,
'session_id': 1,
'topic': 'persona'
}
```
### Data Splits
Number of utterances: 9,869 train, 1,129 validation, and 1,315 test.
## Additional Information
For comprehensive explanations, please check [our paper](https://arxiv.org/pdf/2112.06223.pdf).
### Licensing Information
Creative Common Attribution Share-Alike 4.0 International (CC-BY-SA 4.0)
### Citation Information
If you use our dataset, please cite us:
```
@inproceedings{lovenia2022ascend,
title={ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation},
author={Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others},
booktitle={Proceedings of the 13th Language Resources and Evaluation Conference (LREC)},
year={2022}
``` |