Instructions to use hf-internal-testing/tiny-random-Data2VecAudioForAudioFrameClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecAudioForAudioFrameClassification with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForAudioFrameClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioForAudioFrameClassification") model = AutoModelForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioForAudioFrameClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dca7da9f0d4cac1b28a5478808e5f1a4dd53caaaaa96b4b5173bb75291749f5b
- Size of remote file:
- 294 kB
- SHA256:
- a03ede7bd2a929f33839231ff3f4074c8d499085c20668572d3441f9f5ea0917
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.