LEMAS-Project/LEMAS-Dataset-eval

Overview

This dataset is part of LEMAS-Project(lemas-project.github.io/LEMAS-Project). It contains a large-scale training set (150k+ hours) and a curated evaluation set (500 utterances per language) covering 10 languages, all with word-level alignment.

Fields

• key: unique utterance identifier; the first two characters indicate the language ID
• audio: relative path to the MP3 audio file (in the eval set, this key is renamed to "file_name" for compatibility with the viewer)
• dur: audio duration in seconds
• txt: original transcription
• align: alignment information, including:
  • align.txt: normalized text used for alignment
  • align.words: list of word-level timestamps and confidence scores

Methods

Train Set

• The training set is constructed by filtering large-scale aligned audio–text pairs with language- and dataset-specific constraints.
• URL: https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train

Filtering rules:

• Only samples with successfully extracted word-level alignments are kept; failed alignments are skipped.
• The average alignment confidence score is greater than a threshold x, where x ∈ [0.2, 0.5] depending on the source dataset.
• Audio duration is between 0.5 and 30 seconds, and the maximum pause between consecutive words does not exceed 4 seconds.
• Text is restricted to supported languages; samples containing characters outside the supported set (10 languages) are removed.
• The text-to-duration ratio len(txt) / dur falls within a language-specific range.

Statistics

lang	utterances	total_dur(h)	avg_dur(s)	total_chars	avg_chars	char/sec	total_words	avg_words	word/sec
it	7209567	6116.69	3.054	281128388	38.994	12.7669	48859270	6.777	2.2188
fr	7557428	6535.94	3.113	344633332	45.602	14.6469	65843169	8.712	2.7983
vi	6426292	6600.07	3.697	387621747	60.318	16.3139	89903814	13.990	3.7838
pt	8682442	7384.42	3.062	339417251	39.092	12.7678	60316112	6.947	2.2689
de	11003833	9842.14	3.220	487581808	44.310	13.7612	80119609	7.281	2.2612
id	11659550	11246.27	3.472	543857918	46.645	13.4331	85967394	7.373	2.1234
es	26407271	21224.55	2.893	1011116926	38.289	13.2331	183673862	6.955	2.4038
ru	27474400	22919.31	3.003	991530233	36.089	12.0172	163018329	5.933	1.9758
en	9515267	25347.90	9.590	1419864294	149.220	15.5597	268676221	28.236	2.9443
zh	17776663	32919.28	6.667	2474249286	139.185	20.8781	496957308	27.956	4.1934

Words and chars statistics are computed based on the normalized alignment text (align.txt).

Eval Set

• The eval set is built by filtering, trimming, and ranking aligned audio–text pairs.
• URL: https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-eval

Filtering rules:

• Average word-level alignment score > 0.9
• Number of aligned words > 5
• Duration between 3 and 15 seconds
• Sentence-end silence is trimmed to at most 0.2s

Selection:

• Samples are ranked by
  final_score = edge_gap × density_diff, where
  edge_gap = words[0].start + (dur - words[-1].end) and
  density_diff = |len(align_txt)/dur − global_mean_density|

Statistics

lang	utterances	total_dur(min)	avg_dur(s)	total_chars	avg_chars	char/sec	total_words	avg_words	word/sec
it	500	44.22	5.306	40388	80.78	15.22	6599	13.20	2.49
fr	500	38.17	4.580	38098	76.20	16.64	6546	13.09	2.86
vi	500	36.74	4.409	28546	57.09	12.95	6727	13.45	3.05
pt	500	41.69	5.003	33343	66.69	13.33	5812	11.62	2.32
de	500	38.65	4.638	36571	73.14	15.77	5599	11.20	2.41
id	500	47.20	5.665	41026	82.05	14.49	6133	12.27	2.17
es	500	40.52	4.862	37075	74.15	15.25	6216	12.43	2.56
ru	500	40.24	4.828	33886	67.77	14.04	5138	10.28	2.13
en	500	67.46	8.095	62449	124.90	15.43	11325	22.65	2.80
zh	500	75.84	9.101	95669	191.34	21.02	18627	37.25	4.09

Statistics are computed based on trimmed audio and normalized alignment text (align.txt).

Citation

https://arxiv.org/abs/2601.04233

@article{zhao2026lemas,
  title={LEMAS: A 150K-Hour Large-scale Extensible Multilingual Audio Suite with Generative Speech Models},
  author={Zhao, Zhiyuan and Lin, Lijian and Zhu, Ye and Xie, Kai and Liu, Yunfei and Li, Yu},
  journal={arXiv preprint arXiv:2601.04233},
  year={2026}
}