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README.md

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+---
+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
+---
+
+# 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)

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