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ssl — self-supervised HuBERT–CTC encoder pre-trained on 700,000 hours of Russian speechctc — ASR model fine-tuned with a CTC decoderrnnt — ASR model fine-tuned with an RNN-T decodere2e_ctc — end-to-end CTC model with punctuation and text normalizatione2e_rnnt — end-to-end RNN-T model with punctuation and text normalizationGigaAM-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.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 |
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 70:30.1from transformers import AutoModel
2
3revision = "e2e_rnnt" # can be any v3 model: ssl, ctc, rnnt, e2e_ctc, e2e_rnnt
4model = AutoModel.from_pretrained(
5 "ai-sage/GigaAM-v3",
6 revision=revision,
7 trust_remote_code=True,
8)
9
10transcription = model.transcribe("example.wav")
11print(transcription)torch==2.8.0, torchaudio==2.8.0transformers==4.57.1pyannote-audio==4.0.0, torchcodec==0.7.0hydra-core, omegaconf, sentencepiece