Instructions to use hf-tiny-model-private/tiny-random-WhisperModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-WhisperModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-WhisperModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WhisperModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-WhisperModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5adb0f47da973f3c24eeeb1e54615f042290d9fb20e2f4cf1b4465c1820a0fa
- Size of remote file:
- 3.31 MB
- SHA256:
- 0a38611ea141dfb3a5d0425582abfb852d0ee8d6b938cbdaf433d5607ea010ed
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