README.md

---
license: cc-by-4.0
language:
- mk
library_name: speechbrain
metrics:
- wer
- cer
pipeline_tag: automatic-speech-recognition
base_model:
- jonatasgrosman/wav2vec2-large-xlsr-53-russian
---

# Fine-tuned XLSR-53-russian large model for speech recognition in Macedonian

Authors:
1. Dejan Porjazovski
2. Ilina Jakimovska
3. Ordan Chukaliev
4. Nikola Stikov

This collaboration is part of the activities of the Center for Advanced Interdisciplinary Research (CAIR) at UKIM.

## Data used for training

In training of the model, we used the following data sources:
1. Digital Archive for Ethnological and Anthropological Resources (DAEAR) at the Institutе of Ethnology and Anthropology, PMF, UKIM.
2. Audio version of the international journal "EthnoAnthropoZoom" at the Institutе of Ethnology and Anthropology, PMF, UKIM.
3. The podcast "Обични луѓе" by Ilina Jakimovska.
4. The scientific videos from the series "Наука за деца", foundation KANTAROT.
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

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 it:
```
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
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
asr_classifier = foreign_class(source="Macedonian-ASR/wav2vec2-aed-macedonian-asr", pymodule_file="custom_interface.py", classname="ASR")
asr_classifier = asr_classifier.to(device)
predictions = asr_classifier.classify_file("audio_file.wav", device)
print(predictions)
```

## Training

To fine-tune this model, you need to run:
```
python train.py hyperparams.yaml
```

```train.py``` file contains the functions necessary for training the model and ```hyperparams.yaml``` contains the hyperparameters. For more details about training the model, you can check the [SpeechBrain](https://speechbrain.github.io) documentation.