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.

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.

FP8 quantization

The e2e_ctc, ctc, e2e_rnnt, and rnnt branches additionally carry FP8 (E4M3) quantized weights alongside the original fp16 weights:

• model.safetensors — original fp16 weights
• model_fp8.safetensors — FP8 E4M3 weights (per-output-channel scales) + per-tensor activation scales (model_fp8.safetensors.activation_scales.json)

Quantization targets the GEMM layers (encoder feed-forward and attention projections; RNNT joint enc/pred). All variants use post-training quantization (PTQ) with per-tensor activation calibration — no fine-tuning is required. FP8 PTQ tracks the fp16 model closely for both CTC and RNNT.

Measured over 1000 held-out audio samples, FP8 transcription closely tracks the fp16 baseline — transcripts are identical for 93–99% of samples, and FP8 WER vs ground truth stays within ±0.2% of fp16:

License: MIT

Paper: GigaAM: Efficient Self-Supervised Learner for Speech Recognition (InterSpeech 2025)