Macedonian-ASR/wav2vec2-aed-macedonian-asr

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.

Note

This is an older version (Buki 1.0). It is recommended to use the latest version, trained with much more data: Macedonian-ASR/buki-wav2vec2-2.0

Data used for training

The model is trained on around 60 hours of Macedonian speech.

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.

Results

The results are reported on all the test sets combined and without an external language model.

WER: 13.77
CER: 5.03

Usage

The model is developed using the SpeechBrain 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 the foreign_class function from speechbrain.pretrained.interfaces that allows you to load your custom model.

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, refer to the SpeechBrain documentation.