Instructions to use Jethuestad/dat259-wav2vec2-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jethuestad/dat259-wav2vec2-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Jethuestad/dat259-wav2vec2-en")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Jethuestad/dat259-wav2vec2-en") model = AutoModelForCTC.from_pretrained("Jethuestad/dat259-wav2vec2-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81c12fc7895ea754d9969174a783c0095e317fb3d0c41a470f4a39f24f017612
- Size of remote file:
- 378 MB
- SHA256:
- f15779f3428ab60cd917c7101ca33e66a7f9e4cf15a009d330cd1b5ff460265f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.