Update README.md

README.md

@@ -31,6 +31,28 @@ In training of the model, we used the following data sources:
 5. Macedonian version of the Mozilla Common Voice (version 18).
 
 
+## Model description
+
+This model is an attention-based encoder-decoder (AED). The encoder is a Wav2vec2 model and the decoder is RNN-based.
+
 ## Usage
 
-When using this model, make sure that your speech input is sampled at 16kHz.
+The model is developed using the [SpeechBrain] (https://speechbrain.github.io) toolkit. To use it, you need to install SpeechBrain with:
+```
+pip install speechbrain
+```
+SpeechBrain relies on the Transformers library, therefore you need install that library as well with:
+```
+pip install transformers```
+
+An external `py_module_file=custom_interface.py` is used as an external Predictor class into this HF repos. We use `foreign_class` function from `speechbrain.pretrained.interfaces` that allow you to load you custom model. 
+
+```python
+from speechbrain.inference.interfaces import foreign_class
+classifier_test = foreign_class(source="Macedonian-ASR/wav2vec2-aed-macedonian-asr", pymodule_file="custom_interface.py", classname="ASR")
+classifier_test = classifier_test.to(device)
+predictions = classifier_test.classify_file("/m/triton/scratch/elec/t405-puhe/p/porjazd1/macedonian_asr/data/youtube_audio/audio/vesti_2.m4a", device)
+print(predictions)
+```
+
+