| --- |
| license: mit |
| language: |
| - ru |
| - en |
| pipeline_tag: automatic-speech-recognition |
| --- |
| |
| # 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) |
