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:
- 6b19d603bc8d1547692f8a12db32165697a3d734998377fd526dcf75cd70b92d
- Size of remote file:
- 1.45 MB
- SHA256:
- f0429a957a0939154b3129fda79b055cdcc097c4c5adfe60d017b17a289e9c8b
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