Benchmark Almanac

Multilingual LibriSpeech

Models transcribe read audiobook speech in eight languages from LibriVox. Scored by word error rate.

Published at Interspeech 2020. Launch posts often report a subset of languages.

Released
December 2020
Kind
Speech recognition and translation
Level
Everyday
Scored
Checked automatically
Answers
Open-ended
Tasks
All public
Last in a launch post
December 2025, dropped before it saturated
Last model added
December 2025

In the authors’ words

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages, including about 44.5K hours of English and a total of about 6K hours for other languages. Additionally, we provide Language Models (LM) and baseline Automatic Speech Recognition (ASR) models and for all the languages in our dataset. We believe such a large transcribed dataset will open new avenues in ASR and Text-To-Speech (TTS) research. The dataset will be made freely available for anyone at http://www.openslr.org.

Abstract of MLS: A Large-Scale Multilingual Dataset for Speech Research (arXiv:2012.03411, CC0)
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
2
System cards report it, from 2 labs

Nova 2 Omni Preview leads with 4.70

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 Nova 2 Omni Preview 4.70

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Nova 2 Omni Preview 4.70 Dec 2025 Lab model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
4.70 2026 4.70 2026

Source: Labs’ model cards

Labs headline it less often now, last in December 2025

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Amazon
DateLaunch postScoreAs the post writes it
Dec 2, 2025 Nova 2 Omni Preview 4.7 MLS WER (English, Spanish, French, German, Italian)

2 system cards report it, from 2 labs

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
Dec 2, 2025 Nova 2 Omni Preview technical report 4.7 MLS WER, English, Spanish, French, German and Italian, lower is better (Table 7) Capabilities
Jul 17, 2025 Voxtral Small technical report MLS WER per language, lower is better: nl 9.43, fr 3.73, de 5.57, it 8.44, pt 5.85, es 3.62 (Table 6) Capabilities

More in Audio and video

All 232 in Audio and video
  • Video-MMMU In use 300 expert-level lecture videos with questions testing perception, comprehension and adaptation of knowledge. Accuracy. Qwen 3.8 Max 88.7%
  • LVBench In use Hours-long video understanding with multiple-choice questions on reasoning, grounding and summarization. Gemini 4 Argon 91.7%
  • MMVU In use 3,000 expert-annotated questions on videos across 27 disciplines. Multiple-choice and open-ended accuracy. Qwen 3.8 Max 82.4%
  • MLVU In use Multi-task long video understanding (minutes to hours) with multiple-choice and generation tasks. Accuracy. Qwen 3.8 Max 90.8%
  • FLEURS In use Models transcribe read speech in 102 languages built from FLoRes-101 sentences. Model cards usually report word error rate, so lower scores are better. Phi-4-multimodal 4.00%
  • VoiceBench In use Spoken instructions with speaker, environment and content variation. Voice assistant responses scored by GPT judge or rules. Qwen3.5-Omni-Plus 93.1%