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:
- 691e8d9c63df03d0a7d0456988f0b7afc6bc3c62630f8d2e9f0a2aa894c226dc
- Size of remote file:
- 196 kB
- SHA256:
- 16557ffaa62ad40659c9b96b40c7c883922f6826f4ae4208e15208c430b00a07
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