GigaAM v3 EU AI Act Compliance Profile

GigaAM-v3

GigaAM-v3 is a Conformer-based foundation model with 220–240M parameters, pretrained on diverse Russian speech data using the HuBERT-CTC objective. It is the third generation of the GigaAM family and provides state-of-the-art performance on Russian ASR across a wide range of domains.

GigaAM-v3 includes the following model variants:

• ssl — self-supervised HuBERT–CTC encoder pre-trained on 700,000 hours of Russian speech
• ctc — ASR model fine-tuned with a CTC decoder
• rnnt — ASR model fine-tuned with an RNN-T decoder
• e2e_ctc — end-to-end CTC model with punctuation and text normalization
• e2e_rnnt — end-to-end RNN-T model with punctuation and text normalization

GigaAM-v3 training incorporates new internal datasets: callcenter conversations, speech with background music, natural speech, and speech with atypical characteristics. the models perform on average 30% better on these new domains, while maintaining the same quality as previous GigaAM generations on public benchmarks.

The table below reports the Word Error Rate (%) for GigaAM-v3 and other existing models over diverse domains.

| Set Name | V3_CTC | V3_RNNT | T-One + LM | Whisper | |:------------------|-------:|--------:|-----------:|--------:| | Open Datasets | 3.0 | 2.6 | 5.7 | 12.0 | | Golos Farfield | 4.5 | 3.9 | 12.2 | 16.7 | | Natural Speech | 7.8 | 6.9 | 14.5 | 13.6 | | Disordered Speech | 20.6 | 19.2 | 51.0 | 59.3 | | Callcenter | 10.3 | 9.5 | 13.5 | 23.9 | | Average | 9.2| 8.4 | 19.4 | 25.1 |

The end-to-end ASR models (e2e_ctc and e2e_rnnt) produce punctuated, normalized text directly. In end-to-end ASR comparisons of e2e_ctc and e2e_rnnt against Whisper-large-v3, using Gemini 2.5 Pro as an LLM-as-a-judge, GigaAM-v3 models win by an average margin of 70:30.

For detailed results, see [metrics](https://github.com/sa

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