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:
- 2dfacba5b92380534bdae10e23d4cdc3a8ab67cf0aebf6227d3949a1a7498cd5
- Size of remote file:
- 5.3 kB
- SHA256:
- 36d935a26667d1ae75c651896646bd751f8f4b312ecb1b8c6795265708d78a79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.