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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...

Sourcehuggingface.co/ai-sage/GigaAM-Multilingual/tree/2f8a57144e6ec3adfd32fe0484d9ea9913305bc8

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File size: 842 MB
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Updated: Sep 27, 2026
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Source: Hugging Face

Source description

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 (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}
}

Sources

1 source

Hugging FacePrimaryVersion 2f8a57144e6ec3adfd32fe0484d9ea9913305bc8

huggingface.co/ai-sage/GigaAM-Multilingual/tree/2f8a57144e6ec3adfd32fe0484d9ea9913305bc8

Verified Aug 30

Model artifacts

1 artifact

pytorch_model.bin

bin · 842 MB · SHA-256 e1db43873ec5…1728 · Hugging Face

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Source excerpts

2 excerpts

ReadmeAug 30huggingface.co/ai-sage/GigaAM-Multilingual

--- 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...

NoteAug 30huggingface.co/ai-sage/GigaAM-Multilingual

Source context: 104033 downloads · 81 likes · Pipeline automatic-speech-recognition · Repo ai-sage/GigaAM-Multilingual