Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- ru
+pipeline_tag: automatic-speech-recognition
+library_name: transformers
+tags:
+- asr
+- gigaam
+- stt
+- audio
+- speech
+- rnnt
+- transducer
+---
+
+# GigaAM-v2-CTC 🤗 Hugging Face transformers
+
+* original git https://github.com/salute-developers/GigaAM
+
+Russian ASR model GigaAM-v2-RNNT.
+
+## Model info
+This is an original GigaAM-v2-RNNT with `transformers` library interface.
+
+File [`gigaam_transformers.py`](https://huggingface.co/waveletdeboshir/gigaam-rnnt/blob/main/gigaam_transformers.py) contains model, feature extractor and tokenizer classes with usual transformers methods. Model can be initialized with transformers auto classes (see an example below).
+
+## Installation
+
+my lib versions:
+* `torch` 2.5.1
+* `torchaudio` 2.5.1
+* `transformers` 4.49.0
+
+## Usage
+Usage is same as other `transformers` ASR models.
+
+```python
+from transformers import AutoModel, AutoProcessor
+import torch
+import torchaudio
+
+# load audio
+wav, sr = torchaudio.load("audio.wav")
+# resample if necessary
+wav = torchaudio.functional.resample(wav, sr, 16000)
+
+# load model and processor
+processor = AutoProcessor.from_pretrained("waveletdeboshir/gigaam-rnnt", trust_remote_code=True)
+model = AutoModel.from_pretrained("waveletdeboshir/gigaam-rnnt", trust_remote_code=True)
+model.eval()
+
+input_features = processor(wav[0], sampling_rate=16000, return_tensors="pt")
+
+# greedy prediction
+with torch.no_grad():
+    pred_ids = model.generate(**input_features)
+
+# decode token ids to text
+transcription = processor.batch_decode(pred_ids)[0]
+
+```
+
+## Fine-tune