Instructions to use kdcyberdude/w2v-bert-punjabi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kdcyberdude/w2v-bert-punjabi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kdcyberdude/w2v-bert-punjabi")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kdcyberdude/w2v-bert-punjabi") model = AutoModelForCTC.from_pretrained("kdcyberdude/w2v-bert-punjabi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a106d89fdb23580b5676d830aac29831c823e30aaecba9fa42068e3a0c89a771
- Size of remote file:
- 2.46 GB
- SHA256:
- b2dacf8a91a3924a029c9c2836db8930afdaed87f92c92aea08f3e5f841ac3b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.