Instructions to use hf-internal-testing/tiny-random-WhisperForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WhisperForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-WhisperForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WhisperForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-WhisperForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51da83746945632c295590e26af96ce8624f50691f639a9a2a1830f8ae7bf5cb
- Size of remote file:
- 70.5 kB
- SHA256:
- fdfb24fc30f9265ce27691c4bf9a5c2d3830160dc2f4c3faa5147fc81fde591b
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