license: apache-2.0
datasets:
- mozilla-foundation/common_voice_17_0
language:
- lv
pipeline_tag: automatic-speech-recognition
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 17.0, and a Latvian broadcast dataset LATE-Media.
We also provide a 5-bit quantized version of the model in the GGML format.
Training
Fine-tuning was done using the Hugging Face Transformers library with a modified seq2seq script.
Training data:
| Dataset | Hours |
|---|---|
| Latvian CV 17.0 train set (the V1 split) | 167 |
| LATE-Media train set | 42 |
| Total | 209 |
Evaluation
The model is evaluated on both a Latvian CV 17.0 test set (the V1 split) and a LATE-Media test set.
| Dataset | WER | CER |
|---|---|---|
| Latvian CV 17.0 V1 - formatted | 5.0 | 1.6 |
| Latvian CV 17.0 V1 - normalized | 3.4 | 1.0 |
| LATE-Media 1.0 - formatted | 20.8 | 8.2 |
| LATE-Media 1.0 - normalized | 14.1 | 5.9 |
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). We are grateful to all the participants of the national initiative BalsuTalka.lv for helping to make the Latvian Common Voice dataset much larger and more diverse.