Instructions to use slprl/WhiStress with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slprl/WhiStress with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="slprl/WhiStress")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("slprl/WhiStress", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classifier.pt from slprl/WhiStress: direct link, hf CLI and curl.
- Browser
- Download file 4.74 MB
-
https://huggingface.co/slprl/WhiStress/resolve/main/classifier.pt
- Command line
-
hf download hf://slprl/WhiStress/classifier.pt
-
curl -L -o classifier.pt https://huggingface.co/slprl/WhiStress/resolve/main/classifier.pt
4.74 MB
- Xet hash:
- ae9a2f89418f04a65d5e5ad27888902d317ea8e87cb5dc963b9336b9cb7cfa3b
- Size of remote file:
- 4.74 MB
- SHA256:
- 599257b647cbca9fc21aac4ede87651cd43d03c3338e705bd59d919ee19ebc6f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.