---

license: mit

language:

• ru

pipeline_tag: automatic-speech-recognition

tags:

• audio
• speech-recognition
• transcription
• russian
• ggml
• gguf
• gigaam
• conformer
• rnn-t
• ctc

library_name: ggml

base_model: ai-sage/GigaAM-v3

---

GigaAM-v3 — GGUF (ggml conversions)

GGUF conversions of ai-sage/GigaAM-v3

for use with the gigaam backend in

CrispStrobe/CrispASR.

GigaAM-v3 is a 220 M-parameter Conformer foundation model for Russian ASR,

pretrained with a HuBERT-CTC objective on ~700 K hours of Russian speech. The

upstream repo ships five checkpoints as git revisions; the four ASR ones are

converted here (the ssl encoder has no head and produces no transcript).

| File | Size | Head | Vocabulary | Output |

|---|---|---|---|---|

| gigaam-v3-e2e-rnnt-{f16,q8_0,q4_k}.gguf | 452 / 249 / 154 MB | RNN-T | SentencePiece 1024 | punctuation + casing + ITN — best WER (8.4 % avg) |

| gigaam-v3-e2e-ctc-{f16,q8_0,q4_k}.gguf | 449 / 247 / 152 MB | CTC | SentencePiece 256 | punctuation + casing + ITN, faster decode |

| gigaam-v3-rnnt-{f16,q8_0,q4_k}.gguf | 449 / 246 / 152 MB | RNN-T | 33 Cyrillic chars | lowercase, no punctuation |

| gigaam-v3-ctc-{f16,q8_0,q4_k}.gguf | 449 / 246 / 151 MB | CTC | 33 Cyrillic chars | lowercase, no punctuation |

Which one to pick

gigaam-v3-e2e-rnnt-q8_0.gguf unless you have a reason not to — it is the

lowest-WER variant, emits punctuation and casing, and its transcript is

identical to the PyTorch reference.

Usage

crispasr --backend gigaam -m gigaam-v3-e2e-rnnt-q8_0.gguf -f audio.wav
# or let the registry fetch it:
crispasr --backend gigaam -m auto --auto-download -f audio.wav

Audio is 16 kHz mono. Long inputs are sliced by the CLI's VAD/chunking; the

model itself has a ~25 s practical window (full attention, O(T²)).

Verification

Every file was checked against a per-stage PyTorch reference dumped from the

upstream modeling_gigaam.py (crispasr-diff gigaam <model> <ref> <wav>), on

GigaAM's own example.wav:

| variant | mel | encoder (cos) | transcript vs PyTorch |

|---|---|---|---|

| f16 (all four) | 1.000000 | 1.000000 | byte-identical |

| q8_0 (all four) | 1.000000 | 0.9974 – 0.9988 | byte-identical |

| q4_k ctc, rnnt | 1.000000 | 0.95 – 0.99 | byte-identical |

| q4_k e2e_ctc | 1.000000 | 0.982 | one spurious trailing , |

| q4_k e2e_rnnt | 1.000000 | 0.987 | content identical; 4 words lose their capital letter |

So: q8_0 is the safe quant; q4_k is fine for the charwise models and costs

a little casing/punctuation fidelity on the two SentencePiece ones.

In every quant the mel filterbank, Hann window, encoder.pre.* subsampling

convs and the decode head (joint. / decoder. / head.ctc.*) are kept at

source precision — the mel is un-normalized log-mel, so subsampling rounding

error would otherwise cascade through all 16 conformer blocks, and the head is

a blank-vs-token argmax where a flipped decision derails the greedy decode.

Conversion

python models/convert-gigaam-to-gguf.py \
    --model ai-sage/GigaAM-v3 --revision e2e_rnnt \
    --output gigaam-v3-e2e-rnnt-f16.gguf
./build/bin/crispasr-quantize gigaam-v3-e2e-rnnt-f16.gguf \
    gigaam-v3-e2e-rnnt-q8_0.gguf q8_0

License

MIT, inherited from ai-sage/GigaAM-v3.

Please cite the upstream model when you use these weights.

Provenance and EU AI Act Art. 53 note

• Upstream model: ai-sage/GigaAM-v3 — published by ai-sage.
• Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.