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GigaAM-v3

ai-sage's ai-sage/GigaAM-v3 family ported to transcribe.cpp. Russian-only ASR built around a shared 16-layer Conformer encoder (768-d, 16 heads, rotary positional embeddings), paired with one of two decoder heads (RNN-T or CTC) and trained either end-to-end on cased+punctuated text or on lowercased no-punctuation text with a 33-entry character vocabulary. The four shipped variants are the four cells of that 2×2.

For the architecture deep-dive, validation contract, and porting notes, see the family doc at docs/porting/families/gigaam.md.

Choosing a variant

  • Cased + punctuated Russian, best accuracy. gigaam-v3-e2e-rnnt — RNN-T head trained end-to-end with a 1024-piece SentencePiece vocab.
  • Cased + punctuated Russian, fastest decode. gigaam-v3-e2e-ctc — same Conformer encoder and training data as e2e-rnnt, but a more compact 256-piece SentencePiece vocab, with single-pass CTC alignment instead of the transducer loop.
  • Lowercased no-punct (charwise output). gigaam-v3-rnnt and gigaam-v3-ctc — 33-entry character vocabulary (space + а–я), output is normalized for downstream ASR scoring pipelines that expect this convention. Higher raw WER than the e2e variants because errors on case/punctuation are no longer absorbed by tokenization.
  • The encoder is structurally identical across the four variants but the weights are per-variant fine-tuned — you cannot swap heads at runtime.

All variants

WER is on FLEURS Russian (fleurs-ru) for the Q8_0 preset, measured by transcribe.cpp's WER pipeline. See each per-variant doc for the full quant matrix.

Variant Decoder Output Params Q8_0 size WER (Q8_0) Doc
gigaam-v3-e2e-rnnt RNN-T cased + punctuated ~180M 261 MB 5.36% gigaam-v3-e2e-rnnt.md
gigaam-v3-e2e-ctc CTC cased + punctuated ~180M 260 MB 5.50% gigaam-v3-e2e-ctc.md
gigaam-v3-rnnt RNN-T lowercased, no-punctuation ~180M 260 MB 8.08% gigaam-v3-rnnt.md
gigaam-v3-ctc CTC lowercased, no-punctuation ~180M 259 MB 8.40% gigaam-v3-ctc.md

Pre-built GGUFs for every variant and quant are hosted under handy-computer on Hugging Face; each per-variant doc has direct download links.

Input limits

GigaAM is trained for utterances up to about 25 seconds. Longer audio is accepted, but the library logs a WARN and accuracy may degrade past that window — it is not rejected (upstream GigaAM rejects outright; transcribe.cpp leaves the choice to you). Segment long recordings (e.g. with VAD) for best results. See the input-length contract.

Quick start

Pick a variant and run:

cmake -B build
cmake --build build

build/bin/transcribe-cli \
  -m models/gigaam-v3-e2e-rnnt/gigaam-v3-e2e-rnnt-Q8_0.gguf \
  samples/jfk.wav

The repo doesn't ship the GGUFs — pull them from the corresponding handy-computer/<variant>-gguf repo on Hugging Face, or convert from the upstream ai-sage/GigaAM-v3 checkpoint via the per-variant doc's reproduction section.

Capabilities

All GigaAM-v3 variants support:

  • Transcription of 16 kHz mono WAV input, Russian only.
  • Token-level timestamps at the encoder frame rate (40 ms granularity).

What's not supported (consistent across the family): translation, real-time streaming, VAD, speaker diarization, languages other than Russian, long-form input beyond 25 seconds per utterance (upstream transcribe_longform PyAnnote-VAD chunking is intentionally not ported). See the family doc for the full runtime contract.