Modern speech-to-text model for Armenian language using Wav2Vec2-BERT (see also with quantization and LoRA fine-tuning).
pip install -r requirements.txt
python src/main.py
Best model performance on Armenian Common Voice dataset:
| Metric | Score |
|---|---|
| Word Error Rate (WER) | 14.40% |
| Character Error Rate (CER) | 2.54% |
| Evaluation Loss | 0.1424 |
Pre-trained Model: GoshKolotyan/w2v-bert-2.0-armenian-CV16.0-version-demo
Edit configs/configs.yml for dataset and training settings.
For memory-efficient training, use configs/quantize_configs.yml.
28 commits
Jupyter Notebook
91.2%
Python
8.8%
Modern speech-to-text model for Armenian language using Wav2Vec2-BERT (see also with quantization and LoRA fine-tuning).
pip install -r requirements.txt
python src/main.py
Best model performance on Armenian Common Voice dataset:
| Metric | Score |
|---|---|
| Word Error Rate (WER) | 14.40% |
| Character Error Rate (CER) | 2.54% |
| Evaluation Loss | 0.1424 |
Pre-trained Model: GoshKolotyan/w2v-bert-2.0-armenian-CV16.0-version-demo
Edit configs/configs.yml for dataset and training settings.
For memory-efficient training, use configs/quantize_configs.yml.
28 commits
Jupyter Notebook
91.2%
Python
8.8%