GigaAM Multilingual: Foundation Model for Underrepresented Languages

Andrei Kuzmenko; Alexandr Maximenko; Aleksandr Kutsakov; Georgii Gospodinov; Dmitrii Bolotov; Oleg Kutuzov; Pavel Bogomolov; Fyodor Minkin

arXiv · 2607.10371

GigaAM Multilingual: Foundation Model for Underrepresented Languages

Andrei Kuzmenko·Alexandr Maximenko·Aleksandr Kutsakov·Georgii Gospodinov·Dmitrii Bolotov·Oleg Kutuzov·Pavel Bogomolov·Fyodor Minkin

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Abstract

Despite recent scaling successes, multilingual ASR performance remains highly uneven, with long-tail languages suffering from severe data scarcity. This work addresses the challenge of building robust foundation models for underrepresented Central Asian languages (Kazakh, Kyrgyz, Uzbek). We present GigaAM Multilingual, a Conformer encoder pre-trained on 2M hours of audio using a HuBERT-style objective. Crucially, we introduce a cluster-level data balancing strategy during pre-training and a domain-aware sampling method during fine-tuning to mitigate head-language dominance. In controlled comparisons, our approach outperforms strong open pretrained encoders (Whisper Large v3, Omnilingual-1B) on target languages, achieving significant gains on spontaneous speech while maintaining efficiency. We release the foundation encoder and ASR model, offering a proven recipe for effective multilingual adaptation under realistic data imbalance.

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BibTeX RIS

Andrei Kuzmenko, Alexandr Maximenko, Aleksandr Kutsakov, Georgii Gospodinov, Dmitrii Bolotov, Oleg Kutuzov, Pavel Bogomolov, Fyodor Minkin. 2026-07-11. GigaAM Multilingual: Foundation Model for Underrepresented Languages. https://arxiv.org/abs/2607.10371

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