Instructions to use Yehor/w2v-xls-r-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yehor/w2v-xls-r-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Yehor/w2v-xls-r-uk")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Yehor/w2v-xls-r-uk") model = AutoModelForCTC.from_pretrained("Yehor/w2v-xls-r-uk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
π¨π¨π¨ ATTENTION! π¨π¨π¨
Use an updated model: https://huggingface.co/Yehor/w2v-bert-uk-v2.1
Community
- Discord: https://bit.ly/discord-uds
- Speech Recognition: https://t.me/speech_recognition_uk
- Speech Synthesis: https://t.me/speech_synthesis_uk
See other Ukrainian models: https://github.com/egorsmkv/speech-recognition-uk
Evaluation results
Metrics (float16) using evaluate library with batch_size=1:
- WER: 0.2024 metric, 20.24%
- CER: 0.0364 metric, 3.64%
- Accuracy on words: 79.76%
- Accuracy on chars: 96.36%
- Inference time: 63.4848 seconds
- Audio duration: 16665.5212 seconds
- RTF: 0.0038
Cite this work
@misc {smoliakov_2025,
author = { {Smoliakov} },
title = { w2v-xls-r-uk (Revision 55b6dc0) },
year = 2025,
url = { https://huggingface.co/Yehor/w2v-xls-r-uk },
doi = { 10.57967/hf/4556 },
publisher = { Hugging Face }
}
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Evaluation results
- WER on common_voice_10_0test set self-reported20.240
- CER on common_voice_10_0test set self-reported3.640