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.