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MERaLiON/sea_audiobench_datasets_TLoc

SEA-SpeechBench — TLoc (Temporal Localization) This dataset is the temporal-localization (TLoc) task of SEA-SpeechBench, a large-scale multitask benchmark for speech understanding across Southeast Asia. It contains 6,736 evaluation examples across five languages, each pairing an audio recording of 30–180 seconds with an instruction and a reference answer. Given the recording and the instruction, a model must identify when a described utterance occurs. Quick start… See the full description on the dataset page: https://huggingface.co/datasets/MERaLiON/sea_audiobench_datasets_TLoc.

sourceHugging Facecc-by-nc-nd-4.0updated 15d agoView on Hugging Face

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SEA-SpeechBench — TLoc (Temporal Localization)

This dataset is the temporal-localization (TLoc) task of SEA-SpeechBench, a large-scale multitask benchmark for speech understanding across Southeast Asia. It contains 6,736 evaluation examples across five languages, each pairing an audio recording of 30–180 seconds with an instruction and a reference answer.

Given the recording and the instruction, a model must identify when a described utterance occurs.

Quick start

Requires datasets>=4.0, which decodes audio through torchcodec and needs a system FFmpeg (versions 4–7):

bash

pip install "datasets[audio]"

python

from datasets import load_dataset

ds = load_dataset(
    "MERaLiON/sea_audiobench_datasets_TLoc",
    "tloc_yodas2_en_30",
    split="train",
)

row = ds[0]
print(row["instruction"])
print(row["answer"])
print(row["language"], row["audio_length"])

# `context` is a torchcodec AudioDecoder, not a dict
samples = row["context"].get_all_samples()
waveform = samples.data          # torch.Tensor, shape (num_channels, num_samples)
sr = samples.sample_rate         # 16000
print(waveform.shape, sr)

If you need a numpy array — most feature extractors take one — squeeze the channel dimension (all TLoc audio is mono):

python

audio = samples.data.squeeze(0).numpy()

Subsets

All data lives in a single train split and is intended for evaluation only. Config names follow tloc_{source}_{language}_{context_length_seconds}.

Config	Source	Lang	Ctx (s)	Examples	Nominal hrs
`tloc_sg_streets_30`	SG Streets	en	30	95	0.8
`tloc_sgpccsc_long_30`	ASR-SgpCCSC	zh	30	512	4.3
`tloc_yodas2_en_30`	YODAS2	en	30	1,000	8.3
`tloc_yodas2_id_30`	YODAS2	id	30	1,000	8.3
`tloc_yodas2_th_30`	YODAS2	th	30	1,000	8.3
`tloc_yodas2_vi_30`	YODAS2	vi	30	489	4.1
`tloc_yodas2_vi_60`	YODAS2	vi	60	341	5.7
`tloc_yodas2_vi_120`	YODAS2	vi	120	163	5.4
`tloc_yodas2_vi_180`	YODAS2	vi	180	62	3.1
`tloc_yodas2_zh_30`	YODAS2	zh	30	1,000	8.3
`tloc_yodas2_zh_60`	YODAS2	zh	60	754	12.6
`tloc_yodas2_zh_120`	YODAS2	zh	120	271	9.0
`tloc_yodas2_zh_180`	YODAS2	zh	180	49	2.5
Total				6,736	~80.7

Data fields

Field	Type	Description
`context`	`Audio(sampling_rate=16000)`	The audio recording.
`instruction`	`string`	The instruction given to the model.
`answer`	`string`	The reference answer.
`audio_length`	`float64`	Duration of `context`, in seconds.
`language`	`string`	Language code of the subset.

See the paper for the instruction and answer formats, how segments and annotations were constructed, and the evaluation protocol.

Source data

TLoc introduces no new recordings. All audio comes from existing corpora; the contribution is the segmentation, instructions, and reference answers.

Source	Lang	Upstream license
YODAS2	en, id, zh, th, vi	CC BY 3.0
SG Streets	en	Not specified
ASR-SgpCCSC	zh	CC BY-NC-ND 4.0

Citation

If you use TLoc, please cite our benchmark and the source corpora.

This benchmark

bibtex

@inproceedings{liao2026seaspeechbench,
  title     = {{SEA-SpeechBench}: A Large-Scale Multitask Benchmark for
               Speech Understanding Across Southeast Asia},
  author    = {Liao, Jingyi and Zhang, Wenyu and Liu, Zhuohan and
               He, Yingxu and Lin, Geyu and Zou, Xunlong and Sun, Shuo and
               Alsagoff, Syed Ali Redha and Aw, Ai Ti},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in
               Natural Language Processing (EMNLP)},
  year      = {2026}
}

Source corpora

bibtex

@inproceedings{li2023yodas,
  title        = {YODAS: Youtube-Oriented Dataset for Audio and Speech},
  author       = {Li, Xinjian and Takamichi, Shinnosuke and Saeki, Takaaki
                  and Chen, William and Shiota, Sayaka and Watanabe, Shinji},
  booktitle    = {2023 IEEE Automatic Speech Recognition and Understanding
                  Workshop (ASRU)},
  pages        = {1--8},
  year         = {2023},
  organization = {IEEE}
}

@misc{sgpccsc,
  title  = {{ASR-SgpCCSC: A Singapore Chinese Conversational Speech Corpus}},
  author = {Magic Data Tech},
  year   = {2024},
  url    = {https://magichub.com/datasets/[SLUG]/}
}

@inproceedings{khassanov2019enriching,
  title     = {Enriching Rare Word Representations in Neural Language Models
               by Embedding Matrix Augmentation},
  author    = {Khassanov, Yerbolat and Zeng, Zhiping and Pham, Van Tung and
               Xu, Haihua and Chng, Eng Siong},
  booktitle = {Interspeech 2019},
  pages     = {3505--3509},
  year      = {2019},
  month     = sep,
  publisher = {ISCA},
  doi       = {10.21437/Interspeech.2019-1858},
  url       = {http://dx.doi.org/10.21437/Interspeech.2019-1858}
}