Instructions to use hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration 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-Speech2TextForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-Speech2TextForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- afdd21b9583aafddcbb6ce32d1f958b68923e24e4430d9ace6757d3f7c9a91f9
- Size of remote file:
- 726 kB
- SHA256:
- 2405e0f0f7db2a9a78bb15df24eae0a88a8cc089b3ed19e53e4d218ea4325f84
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