Instructions to use rohitp1/libri-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rohitp1/libri-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rohitp1/libri-finetune")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rohitp1/libri-finetune") model = AutoModelForCTC.from_pretrained("rohitp1/libri-finetune") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e28ee8391763389d01bf369406cd104cc02c1c01d1369414e25a4d1c0b6b38df
- Size of remote file:
- 208 MB
- SHA256:
- c20a79cd972cbbf8f76ce5deca991c8e31c630aa68021584a350bedda29ca852
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.