Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use davidilag/wav2vec2-xls-r-1b-danish-12h-6k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidilag/wav2vec2-xls-r-1b-danish-12h-6k-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="davidilag/wav2vec2-xls-r-1b-danish-12h-6k-steps")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("davidilag/wav2vec2-xls-r-1b-danish-12h-6k-steps") model = AutoModelForCTC.from_pretrained("davidilag/wav2vec2-xls-r-1b-danish-12h-6k-steps") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0806fda54cc65db718864e19f602633f123f111cfe3201aff9156c4f520bd3d
- Size of remote file:
- 3.85 GB
- SHA256:
- 43b39424fa42f8b79226a870ff0d27fb9a0270b45ee6b58b8eae95201f44c93a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.