Instructions to use hf-tiny-model-private/tiny-random-SpeechT5Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SpeechT5Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-SpeechT5Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-SpeechT5Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-SpeechT5Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dc82f60ce6b29d9a35208f0d2bdecdcf961d82cfe1d43b92d72506ab8d06829
- Size of remote file:
- 238 kB
- SHA256:
- 7fcc48f3e225f627b1641db410ceb0c8649bd2b0c982e150b03f8be3728ab560
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