Instructions to use saattrupdan/kblab-voxrex-wav2vec2-large-cv8-da with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saattrupdan/kblab-voxrex-wav2vec2-large-cv8-da with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="saattrupdan/kblab-voxrex-wav2vec2-large-cv8-da")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("saattrupdan/kblab-voxrex-wav2vec2-large-cv8-da") model = AutoModelForCTC.from_pretrained("saattrupdan/kblab-voxrex-wav2vec2-large-cv8-da", device_map="auto") - Notebooks
- Google Colab
- Kaggle
KBLab-VoxRex-Wav2vec2-large-CV8-da
Model description
This model is a fine-tuned version of the Swedish acoustic model KBLab/wav2vec2-large-voxrex on the Danish part of Common Voice 8.0, containing ~6 crowdsourced hours of read-aloud Danish speech.
Performance
The model achieves the following WER scores (lower is better):
| Dataset | WER without LM | WER with 5-gram LM |
|---|---|---|
| Danish part of Common Voice 8.0 | 37.63 | 30.51 |
| Alvenir test set | 35.75 | 28.33 |
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Evaluation results
- wer on Danish Common Voice 8.0self-reported30.510
- wer on Alvenir ASR test datasetself-reported28.330