license: apache-2.0
language:
- lv
pipeline_tag: automatic-speech-recognition
base_model:
- openai/whisper-large-v3
General-purpose Latvian ASR model
This is a fine-tuned whisper-large-v3 model for Latvian, trained by AiLab.lv using two general-purpose speech datasets: the Latvian part of Common Voice 19.0, and the latest version of the Latvian broadcast dataset LATE-Media.
This version of the model supersedes the previous whisper-large-v3-lv-late-cv17 model.
We also provide 4-bit, 5-bit and 8-bit quantized versions of the model in the GGML format for the use with whisper.cpp, as well as an 8-bit quantized version for the use with CTranslate2.
Training
Fine-tuning was done using the Hugging Face Transformers library with a modified seq2seq script.
| Training data | Hours |
|---|---|
| Latvian Common Voice 19.0 train set (the VW split) | 212.6 |
| LATE-Media 2.0 train set | 69.8 |
| Total | 282.4 |
Evaluation
| Testing data | WER | CER |
|---|---|---|
| Latvian Common Voice 19.0 test set (VW) - formatted | 4.8 | 1.6 |
| Latvian Common Voice 19.0 test set (VW) - normalized | 3.2 | 1.0 |
| LATE-Media 1.0 test set - formatted | 19.2 | 7.6 |
| LATE-Media 1.0 test set - normalized | 12.8 | 5.3 |
The Latvian CV 19.0 test set is available here. The LATE-Media 1.0 test set is available here.
Citation
Please cite this paper if you use this model in your research:
@inproceedings{dargis-etal-2024-balsutalka-lv,
author = {Dargis, Roberts and Znotins, Arturs and Auzina, Ilze and Saulite, Baiba and Reinsone, Sanita and Dejus, Raivis and Klavinska, Antra and Gruzitis, Normunds},
title = {{BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages}},
booktitle = {Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)},
publisher = {ELRA and ICCL},
year = {2024},
pages = {2080--2085},
url = {https://aclanthology.org/2024.lrec-main.187}
}
Acknowledgements
This work was supported by the EU Recovery and Resilience Facility project Language Technology Initiative (2.3.1.1.i.0/1/22/I/CFLA/002) in synergy with the State Research Programme project LATE (VPP-LETONIKA-2021/1-0006).