metadata

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

Dataset Description
Usage
Dataset Structure
- Data Splits
Additional Information
- Licensing 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.

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}

Datasets:

CAiRE
/

ASCEND