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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed8fecab6742de6850d368e4a5818a40fff184756378a3436eca83534a72c41d
- Size of remote file:
- 162 kB
- SHA256:
- 07894e07136dcdf2c55854af73a71627c0c44f904cc7d9c080c5301ba05c1303
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