---

license: mit

base_model: ai-sage/GigaAM-v3

base_model_relation: quantized

library_name: transcribe.cpp

pipeline_tag: automatic-speech-recognition

language:

- ru

tags:

- gguf

- transcribe.cpp

- asr

- speech-to-text

- gigaam

- conformer

- ctc

- russian

transcribe_cpp:

wer_fleurs_ru:

f32: 5.5

f16: 5.5

q8_0: 5.5

q6_k: 5.56

q5_k_m: 5.58

q4_k_m: 5.57

rtf_m4_max:

metal: 112

cpu: 27

rtf_ryzen_4750u:

vulkan: 30

cpu: 9

streaming: false

translate: false

lang_detect: false

timestamps: token

---

GigaAM-v3: transcribe.cpp GGUF

GGUF conversions of ai-sage/GigaAM-v3 for use

with transcribe.cpp.

Ported from upstream commit

cec030b,

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 CTC decoding. 16-layer Conformer encoder with a 1×1 Conv1d CTC head. Output is cased Russian with punctuation, decoded from a 256-piece SentencePiece tokenizer.

Downloads

| Quantization | Download | Size | WER (FLEURS ru) |

| --- | --- | ---: | ---: |

| F32 | gigaam-v3-e2e-ctc-F32.gguf | 843 MB | 5.50% |

| F16 | gigaam-v3-e2e-ctc-F16.gguf | 428 MB | 5.50% |

| Q8_0 | gigaam-v3-e2e-ctc-Q8_0.gguf | 260 MB | 5.50% |

| Q6_K | gigaam-v3-e2e-ctc-Q6_K.gguf | 216 MB | 5.56% |

| Q5_K_M | gigaam-v3-e2e-ctc-Q5_K_M.gguf | 195 MB | 5.58% |

| Q4_K_M | gigaam-v3-e2e-ctc-Q4_K_M.gguf | 174 MB | 5.57% |

WER measured on the full FLEURS ru test split (775 utterances) with greedy decoding and no external LM. F32 reference baseline: 5.50%. Upstream gigaam author package measured on the same manifest: 6.93%; 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:

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:

build/bin/transcribe-cli \
  -m gigaam-v3-e2e-ctc-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

> cec030b 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

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)