GigaAM Multilingual
GigaAM Multilingual is a family of Conformer-based foundation models (220M / 600M parameters) pre-trained with a HuBERT-style objective on 2M hours of speech across 70+ languages and fine-tuned for speech recognition with character-wise CTC decoders on 50K hours.
The models provide best-in-class open-source quality on Russian, Kazakh, Kyrgyz, and Uzbek, and moderate quality on English.
GigaAM Multilingual includes the following model variants:
ssl— 220M self-supervised encoderctc— 220M ASR model with a character-wise CTC decoderlarge_ssl— 600M self-supervised encoderlarge_ctc— 600M ASR model with a character-wise CTC decoder
Model Performance
Word Error Rate (%) on Common Voice (CV), FLEURS, and internal in-the-wild test sets. Utterances longer than 30 s and references containing digits are excluded; references/hypotheses are normalized (lowercasing, punctuation removal, numerals→words); greedy decoding. Best per row in bold.
| Language | Dataset | GigaAM Multilingual | GigaAM Multilingual Large | Omnilingual 1B (LLM) | Seamless M4T large v2 | Whisper large v3 |
|---|---|---|---|---|---|---|
| English | CV | 26.0 | 21.5 | 24.7 | 16.2 | 20.0 |
| English | FLEURS | 12.2 | 9.4 | 7.1 | 5.8 | 3.9 |
| Russian | CV | 7.1 | 5.1 | 13.6 | 9.2 | 9.1 |
| Russian | FLEURS | 4.4 | 3.0 | 6.4 | 4.6 | 3.1 |
| Russian | Internal | 7.6 | 6.0 | 14.6 | 16.1 | 10.1 |
| Kazakh | CV | 17.2 | 13.8 | 23.7 | 23.8 | 57.8 |
| Kazakh | FLEURS | 5.2 | 4.4 | 6.6 | 6.8 | 32.4 |
| Kazakh | Internal | 18.8 | 15.8 | 32.2 | 62.9 | 65.2 |
| Kyrgyz | CV | 12.5 | 10.2 | 21.6 | 14.3 | 95.2 |
| Kyrgyz | FLEURS | 7.0 | 5.5 | 8.1 | 9.5 | 86.3 |
| Kyrgyz | Internal | 11.1 | 9.8 | 25.0 | 78.3 | 102.2 |
| Uzbek | CV | 11.3 | 9.2 | 32.8 | 25.1 | 109.9 |
| Uzbek | FLEURS | 10.0 | 7.3 | 15.4 | 11.9 | 105.4 |
| Uzbek | Internal | 13.8 | 12.7 | 30.2 | 40.0 | 120.6 |
Usage
from transformers import AutoModel
revision = "ctc" # any variant: ssl, ctc, large_ssl, large_ctc
model = AutoModel.from_pretrained(
"ai-sage/GigaAM-Multilingual",
revision=revision,
trust_remote_code=True,
)
transcription = model.transcribe("example.wav")
print(transcription)
Recommended versions:
torch==2.10.*,torchaudio==2.10.*transformers==5.*- (any)
hydra-core,omegaconf
Full usage guide can be found in the example.
Fine-tuning to a new language
The ssl / large_ssl backbones can be adapted to a new language — see the fine-tuning guide and the example notebook.
Citation
@misc{gigaam_multilingual,
title={GigaAM Multilingual: Foundation Model for Underrepresented Languages},
author={Andrei Kuzmenko and Alexandr Maximenko and Aleksandr Kutsakov and Georgii Gospodinov and Dmitrii Bolotov and Oleg Kutuzov and Pavel Bogomolov and Fyodor Minkin},
year={2026},
eprint={2607.10371},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2607.10371}
}