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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...
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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:
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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 decoderWord 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 |
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.*hydra-core, omegaconfFull usage guide can be found in the example.
The ssl / large_ssl backbones can be adapted to a new language — see the fine-tuning guide and the example notebook.
@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}
}
--- license: mit language: - ru - en - kk - ky - uz pipeline_tag: automatic-speech-recognition --- # 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 encoder - `ctc` — 220M ASR model with a character-wise CTC decoder - `large_ssl` — 600M self-supervised encoder - `large_ctc` — 600M ASR model with a character-wise CTC decoder ## Model Performance Word Error Rate (%) on [Common Voice](https://commonvoice.mozilla.org) (CV), [FLEURS](https://huggingface.co/datasets/google/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 ```python from transformers import AutoModel revision = "ctc" # any variant: ssl, ctc, large_ssl, large_ctc model = AutoModel.from_pretrained( "a...
Source context: 104033 downloads · 81 likes · Pipeline automatic-speech-recognition · Repo ai-sage/GigaAM-Multilingual
| 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 |