Update README.md

README.md

@@ -1,3 +1,65 @@
----
-license: mit
----
+---
+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)