Update README.md

README.md

@@ -17,6 +17,7 @@ tags:
 [![Use In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/waveletdeboshir/07e39ae96f27331aa3e1e053c2c2f9e8/gigaam-ctc-hf-with-lm.ipynb)
 
 # GigaAM-v2-CTC with ngram LM and beamsearch 🤗 Hugging Face transformers
+This is an **unofficial Transformers wrapper** for the original GigaAM model released by SberDevices.
 
 * original git https://github.com/salute-developers/GigaAM
 * ngram LM from [`bond005/wav2vec2-large-ru-golos-with-lm`](https://huggingface.co/bond005/wav2vec2-large-ru-golos-with-lm)
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 Russian ASR model GigaAM-v2-CTC with external ngram LM and beamsearch decoding.
 
 ## Model info
-This is an original GigaAM-v2-CTC with `transformers` library interface, beamsearch decoding and hypothesis rescoring with external ngram LM.
+This is GigaAM-v2-CTC with `transformers` library interface, beamsearch decoding and hypothesis rescoring with external ngram LM.
 In addition it can be use to extract word-level timestamps.
 
 File [`gigaam_transformers.py`](https://huggingface.co/waveletdeboshir/gigaam-ctc-with-lm/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).