Instructions to use RaivisDejus/whisper-tiny-lv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RaivisDejus/whisper-tiny-lv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RaivisDejus/whisper-tiny-lv")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("RaivisDejus/whisper-tiny-lv") model = AutoModelForMultimodalLM.from_pretrained("RaivisDejus/whisper-tiny-lv") - Notebooks
- Google Colab
- Kaggle
Latvian Whisper tiny speech recognition model
Trained on combination of:
- Common Voice 17, custom selection of all validated clips, max 1000 clips per speaker
- Fleurs, test+train+validation
Both regular whisper model and CTranslate2 converted version for use with faster-whisper as part of Home Assistant Whisper integration are available.
Speech recognition quality is poor, more data is needed, donate your voice on Balsu talka
For better recognition quality use whisper-small-lv model, it is noticeably better and only slightly slower.
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