Fine-tuned XLSR-53-russian large model for speech recognition in Macedonian
Authors:
- Dejan Porjazovski
- Ilina Jakimovska
- Ordan Chukaliev
- 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:
- Digital Archive for Ethnological and Anthropological Resources (DAEAR) at the Institutе of Ethnology and Anthropology, PMF, UKIM.
- Audio version of the international journal "EthnoAnthropoZoom" at the Institutе of Ethnology and Anthropology, PMF, UKIM.
- The podcast "Обични луѓе" by Ilina Jakimovska.
- The scientific videos from the series "Наука за деца", foundation KANTAROT.
- 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 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.
- Downloads last month
- 168
Model tree for Macedonian-ASR/wav2vec2-aed-macedonian-asr
Spaces using Macedonian-ASR/wav2vec2-aed-macedonian-asr 2
Evaluation results
- Test WER on Macedonian Common Voice V.18.0self-reported5.660
- Test CER on Macedonian Common Voice V.18.0self-reported1.430