Instructions to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74d32b8a34ff9727f01229615bdc6ed2db69808b00db187d0c8999d4c08e87a1
- Size of remote file:
- 13 MB
- SHA256:
- 32a4bb6f12e707aef2a4df8bc53203cf49c487c0229d33e9e420408df47b739d
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