Instructions to use hf-internal-testing/tiny-random-WhisperModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WhisperModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-WhisperModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WhisperModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-WhisperModel") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
Browse files[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).
onnx/decoder_model.onnx
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onnx/decoder_model_merged.onnx
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onnx/decoder_with_past_model.onnx
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onnx/encoder_model.onnx
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size 70540
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