Instructions to use hf-tiny-model-private/tiny-random-Data2VecAudioModel 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-Data2VecAudioModel 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-Data2VecAudioModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d072eeb9d551f3b32b1de2cf9e1b5a543a913352f32dc82d44caa65436dc1d1
- Size of remote file:
- 291 kB
- SHA256:
- 003999ac14c6c7aea84f568355a1d9ab6d7d28f4e9cc1f9913ea7934aac3344a
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