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 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 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.
Usage
from transformers import AutoModel
revision = "e2e_rnnt" # can be any v3 model: ssl, ctc, rnnt, e2e_ctc, e2e_rnnt
model = AutoModel.from_pretrained(
"ai-sage/GigaAM-v3",
revision=revision,
trust_remote_code=True,
)
transcription = model.transcribe("example.wav")
print(transcription)
Recommended versions:
torch==2.8.0,torchaudio==2.8.0transformers==4.57.1pyannote-audio==4.0.0,torchcodec==0.7.0- (any)
hydra-core,omegaconf,sentencepiece
Full usage guide can be found in the example.
License: MIT
Paper: GigaAM: Efficient Self-Supervised Learner for Speech Recognition (InterSpeech 2025)