Model reference · open weights
GigaAM is an open-weight audio or speech model from vpermilp. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | vpermilp |
|---|---|
| Type | Audio & music |
| Task | Speech→text |
| Parameters (lead) | 223M |
| Released | 2026-04-17 |
| Popularity | 599 downloads / month |
| Licence | Open weights |
About
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 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.
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.
The e2e_ctc, ctc, e2e_rnnt, and rnnt branches additionally carry FP8 (E4M3) quantized weights alongside the original fp16 weights:
model.safetensors — original fp16 weightsmodel_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:
| Variant | Word disagreement (FP8 vs fp16) | Transcripts identical | ΔWER vs fp16 |
|---|---|---|---|
e2e_ctc | 1.55% | 93.6% | +0.00 |
ctc | 1.59% | 93.4% | +0.06 |
e2e_rnnt | 0.85% | 97.1% | −0.16 |
rnnt | 0.26% | 99.1% | +0.06 |
License: MIT
Paper: GigaAM: Efficient Self-Supervised Learner for Speech Recognition (InterSpeech 2025)
From the published model card. Full card on the HuggingFace links in the sidebar.
How it works
Using it via the API
Once AxForge deploys vpermilp-gigaam for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (vpermilp-gigaam below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \ -H "Authorization: Bearer $AXFORGE_API_KEY" \ -F model="vpermilp-gigaam" -F file=@audio.mp3
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.