Instructions to use aksds/checkpoint-100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aksds/checkpoint-100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aksds/checkpoint-100")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("aksds/checkpoint-100") model = AutoModelForSpeechSeq2Seq.from_pretrained("aksds/checkpoint-100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd1e5450438a87d935762f7102170dc2d8cad5fe06e9900b58f0728d54da7ac5
- Size of remote file:
- 298 MB
- SHA256:
- 5efd59e694c86e6da12a811866b2dc6d2ec5b94d76c2ced495124668b275ef56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.