Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech
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Computer Science > Computation and Language
arXiv:2603.16606 (cs)
[Submitted on 17 Mar 2026 (v1), last revised 18 Jun 2026 (this version, v3)]
Title:Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech
Authors:Omnilingual SONAR Team: João Maria Janeiro, Pere-Lluís Huguet Cabot, Ioannis Tsiamas, Yen Meng, Vivek Iyer, Guillem Ramírez, Loic Barrault, Belen Alastruey, Xiang "Tony" Cao, Yu-An Chung, Marta R. Costa-Jussa, David Dale, Kevin Heffernan, Jaehyeong Jo, Artyom Kozhevnikov, Alexandre Mourachko, Christophe Ropers, Holger Schwenk, Paul-Ambroise Duquenne
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Abstract:Cross-lingual sentence encoders typically cover only a few hundred languages and often trade downstream quality for stronger alignment, limiting their adoption. We introduce OmniSONAR, a new family of omnilingual, cross-lingual and cross-modal sentence embedding models that natively embed text, speech, code, and mathematical expressions in a single semantic space, while delivering state-of-the-art downstream performance at the scale of thousands of languages, from high-resource to extremely low-resource varieties. To reach this scale without representation collapse, we use progressive training. We first learn a strong foundational space for 200 languages with an LLM-initialized encoder-decoder, combining token-level decoding with a novel split-softmax contrastive loss and synthetic hard negatives. Building on this foundation, we expand to several thousands language varieties via a two-stage teacher-student encoder distillation framework. Finally, we demonstrate the cross-modal extensibility of this space by seamlessly mapping 177 spoken languages into it. OmniSONAR halves cross-lingual similarity search error on the 200-language FLORES dataset and reduces error by a factor of 15 on the 1,560-language BIBLE benchmark. It also enables strong translation, outperforming NLLB-3B on multilingual benchmarks and exceeding prior models (including much larger LLMs) by 15 chrF++ points on 1,560 languages into English BIBLE translation. OmniSONAR also performs strongly on MTEB and XLCoST. For speech, OmniSONAR achieves a 43% lower similarity-search error and reaches 97% of SeamlessM4T speech-to-text quality, despite being zero-shot for translation (trained only on ASR data). Finally, by training an encoder-decoder LM, Spectrum, exclusively on English text processing OmniSONAR embedding sequences, we unlock high-performance transfer to thousands of languages and speech for complex downstream tasks.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.16606 [cs.CL] |
| (or arXiv:2603.16606v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.16606
arXiv-issued DOI via DataCite
|
Submission history
From: João Maria Janeiro [view email][v1] Tue, 17 Mar 2026 14:47:35 UTC (31,082 KB)
[v2] Wed, 18 Mar 2026 15:24:53 UTC (31,082 KB)
[v3] Thu, 18 Jun 2026 13:24:02 UTC (9,840 KB)
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