Instructions to use oyvindgrutle/whisper-large-amk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oyvindgrutle/whisper-large-amk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="oyvindgrutle/whisper-large-amk")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("oyvindgrutle/whisper-large-amk") model = AutoModel.from_pretrained("oyvindgrutle/whisper-large-amk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from oyvindgrutle/whisper-large-amk: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/oyvindgrutle/whisper-large-amk/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://oyvindgrutle/whisper-large-amk/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/oyvindgrutle/whisper-large-amk/resolve/main/pytorch_model.bin
6.17 GB
- Xet hash:
- d7bdc6e24fd123b41a8e720ae91992626cd7daaece01d0c8e2bdc939735c988d
- Size of remote file:
- 6.17 GB
- SHA256:
- 470f24682fe2bb8c7fb72a33e39b993d985e36401ee5e25a9a1ca83e8fcd5556
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