Update README.md

README.md

@@ -1,3 +1,52 @@
----
-license: mit
----
+---
+license: mit
+language:
+- ru
+pipeline_tag: automatic-speech-recognition
+library_name: transformers
+tags:
+- asr
+---
+
+# GigaAMv2-CTC Hugging Face transformers
+
+* original git https://github.com/salute-developers/GigaAM
+
+Russian ASR model
+
+## Model info
+This is an original GigaAMv2-CTC with `transformers` library interface.
+
+File `gigaam_transformers.py` contains model, feature extractor and tokenizer classes with usual transformers methods.
+
+Jupyter `GigaAMHFTrain.ipynb` contains training pipeline with `transformers`.
+
+## Usage
+Usage is same as for other `transformers` asr models.
+
+```python
+>>> from gigaam_transformers import GigaAMCTCHF, GigaAMProcessor
+>>> 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 = GigaAMProcessor.from_pretrained("waveletdeboshir/gigaam-ctc")
+>>> model = GigaAMCTCHF.from_pretrained("waveletdeboshir/gigaam-ctc")
+
+>>> input_features = processor(wav[0], sampling_rate=16000, return_tensors="pt")
+
+>>> # predict
+>>> pred = model(input_features)
+>>> # greedy decoding
+>>> greedy_ids = pred.predictions.argmax(dim=-1)
+>>> # decode token ids to text
+>>> transcription = processor.batch_decode(greedy_ids)
+
+
+```
+
+## Finetune