--- 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 - rnnt - russian transcribe_cpp: wer_fleurs_ru: f32: 8.08 f16: 8.08 q8_0: 8.08 q6_k: 8.07 q5_k_m: 8.12 q4_k_m: 8.12 rtf_m4_max: metal: 110 cpu: 27 rtf_ryzen_4750u: vulkan: 25 cpu: 9 streaming: false translate: false lang_detect: false timestamps: token --- # GigaAM-v3: transcribe.cpp GGUF GGUF conversions of [ai-sage/GigaAM-v3](https://huggingface.co/ai-sage/GigaAM-v3) for use with [transcribe.cpp](https://github.com/handy-computer/transcribe.cpp). Ported from upstream commit [c7f128b](https://huggingface.co/ai-sage/GigaAM-v3/commit/c7f128b), pinned 2026-05-12. Validated against the gigaam author package reference at transcribe.cpp commit [42b96d9](https://github.com/handy-computer/transcribe.cpp/tree/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](https://huggingface.co/handy-computer/gigaam-v3-rnnt-gguf/resolve/main/gigaam-v3-rnnt-F32.gguf) | 846 MB | 8.08% | | F16 | [gigaam-v3-rnnt-F16.gguf](https://huggingface.co/handy-computer/gigaam-v3-rnnt-gguf/resolve/main/gigaam-v3-rnnt-F16.gguf) | 430 MB | 8.08% | | Q8_0 | [gigaam-v3-rnnt-Q8_0.gguf](https://huggingface.co/handy-computer/gigaam-v3-rnnt-gguf/resolve/main/gigaam-v3-rnnt-Q8_0.gguf) | 260 MB | 8.08% | | Q6_K | [gigaam-v3-rnnt-Q6_K.gguf](https://huggingface.co/handy-computer/gigaam-v3-rnnt-gguf/resolve/main/gigaam-v3-rnnt-Q6_K.gguf) | 217 MB | 8.07% | | Q5_K_M | [gigaam-v3-rnnt-Q5_K_M.gguf](https://huggingface.co/handy-computer/gigaam-v3-rnnt-gguf/resolve/main/gigaam-v3-rnnt-Q5_K_M.gguf) | 196 MB | 8.12% | | Q4_K_M | [gigaam-v3-rnnt-Q4_K_M.gguf](https://huggingface.co/handy-computer/gigaam-v3-rnnt-gguf/resolve/main/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: ```bash ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav ``` See the [transcribe.cpp model page](https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/gigaam-v3-rnnt.md) for performance numbers, numerical validation, and reproduction steps. ## License Inherited from the base model: **MIT**. See the [upstream model card](https://huggingface.co/ai-sage/GigaAM-v3) for full terms. --- ## Original Model Card > The section below is reproduced from > [ai-sage/GigaAM-v3](https://huggingface.co/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](https://github.com/salute-developers/GigaAM/blob/main/evaluation.md). ## 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](https://github.com/salute-developers/GigaAM/blob/main/colab_example.ipynb). **License:** MIT **Paper:** [GigaAM: Efficient Self-Supervised Learner for Speech Recognition (InterSpeech 2025)](https://arxiv.org/abs/2506.01192)