Instructions to use hf-internal-testing/tiny-random-SpeechT5Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SpeechT5Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SpeechT5Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SpeechT5Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SpeechT5Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e67d24b27512c3c91e95d118c552e3b95886c26ac4f786aac129cb613574f12
- Size of remote file:
- 162 kB
- SHA256:
- 4a18ea9332a69e27a18d7b35d7e62339fc5c34fc51a802b5909d94249d69c6eb
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