Datasets:
license: cc0-1.0
language:
- zlm
pretty_name: Sarawak Malay
task_categories:
- speech-recognition
- text-to-speech
tags:
- speech-recognition
- text-to-speech
This is a Sarawak Malay conversation data for the purpose of speech technology research. At the moment, this is an experimental data and currently used for investigating speaker diarization. The data was collected by Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak. The data consists of 38 conversations that have been transcribed using Transcriber (see TextGrid folder), where each file contains two speakers. Each conversation was recorded by different individuals using microphones from mobile devices or laptops thus, different file formats were collected from the data collectors. All data was then standardized to mono, 16000Khz, wav format.
Languages
zlm
Supported Tasks
Speech Recognition, Text To Speech
Dataset Usage
Using datasets library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/sarawak_malay", trust_remote_code=True)
Using seacrowd library
# Load the dataset using the default config
dset = sc.load_dataset("sarawak_malay", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("sarawak_malay"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
More details on how to load the seacrowd library can be found here.
Dataset Homepage
https://github.com/sarahjuan/sarawakmalay
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
Creative Commons Zero v1.0 Universal (cc0-1.0)
Citation
If you are using the Sarawak Malay dataloader in your work, please cite the following:
@INPROCEEDINGS{
10337314,
author={Rahim, Mohd Zulhafiz and Juan, Sarah Samson and Mohamad, Fitri Suraya},
booktitle={2023 International Conference on Asian Language Processing (IALP)},
title={Improving Speaker Diarization for Low-Resourced Sarawak Malay Language Conversational Speech Corpus},
year={2023},
pages={228-233},
keywords={Training;Oral communication;Data models;Usability;Speech processing;Testing;Speaker diarization;x-vectors;clustering;low-resource;auto-labeling;pseudo-labeling;unsupervised},
doi={10.1109/IALP61005.2023.10337314}}
@article{lovenia2024seacrowd,
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
year={2024},
eprint={2406.10118},
journal={arXiv preprint arXiv: 2406.10118}
}