Instructions to use hf-internal-testing/tiny-random-Data2VecAudioModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecAudioModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Data2VecAudioModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioModel") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#2
by Xenova HF Staff - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:19af0b56e5ff948d6e6e2c6e822d84149af73f1fe45687dad0dc7bd1e587c532
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size 420682
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