Instructions to use hf-internal-testing/tiny-random-WhisperForAudioClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WhisperForAudioClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-WhisperForAudioClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WhisperForAudioClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-WhisperForAudioClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0336057f5ed86fba77aad62a8f8a9aa973c4c64db1d685a9bca1b5ccf9f6daf0
- Size of remote file:
- 66 kB
- SHA256:
- 9b8549f7d1a5ca1e7094942f8cae71468926a20bf2d23537d6ace9bb6c2e34f8
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