Ported from upstream commit c7f128b, pinned 2026-05-12. Validated against the gigaam author package reference at transcribe.cpp commit 42b96d9 on 2026-05-12.

Offline Russian speech-to-text with greedy RNN-T decoding. Same 16-layer Conformer encoder as the e2e variant, fine-tuned to emit lowercased Russian with no punctuation; 33-entry character vocabulary.

Downloads

Quantization	Download	Size	WER (FLEURS ru)
F32	gigaam-v3-rnnt-F32.gguf	846 MB	8.08%
F16	gigaam-v3-rnnt-F16.gguf	430 MB	8.08%
Q8_0	gigaam-v3-rnnt-Q8_0.gguf	260 MB	8.08%
Q6_K	gigaam-v3-rnnt-Q6_K.gguf	217 MB	8.07%
Q5_K_M	gigaam-v3-rnnt-Q5_K_M.gguf	196 MB	8.12%
Q4_K_M	gigaam-v3-rnnt-Q4_K_M.gguf	175 MB	8.12%

WER measured on the full FLEURS ru test split (775 utterances) with greedy decoding and no external LM. F32 reference baseline: 8.08%. Upstream gigaam author package measured on the same manifest: 9.46%; the 1.4 pp gap is upstream rejecting 5 long (>25 s) utterances with Too long wav file, use 'transcribe_longform' method. (counted as 100% deletion errors). On the 770-utt subset both sides decode, transcribe.cpp matches upstream exactly. ai-sage does not publish a FLEURS ru WER; this number is measured here.

Usage

Build transcribe.cpp from source:

bash

git clone git@github.com:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build

Run on a 16 kHz mono WAV:

bash

build/bin/transcribe-cli \
  -m gigaam-v3-rnnt-Q8_0.gguf \
  input.wav

If your audio isn't already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

See the transcribe.cpp model page for performance numbers, numerical validation, and reproduction steps.

License

Inherited from the base model: MIT. See the upstream model card for full terms.

Original Model Card

The section below is reproduced from ai-sage/GigaAM-v3 at commit c7f128b for offline reference. The upstream card is the authoritative source.

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.

Usage

python

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==2.8.0
transformers==4.57.1
pyannote-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)

handy-computer/gigaam-v3-rnnt-gguf

GigaAM-v3: transcribe.cpp GGUF

Downloads

Usage

License

Original Model Card

GigaAM-v3

Usage

handy-computer/
gigaam-v3-rnnt-gguf