Instructions to use hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification") model = AutoModelForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-UniSpeechSatForAudioFrameClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be59429d707d54bbf7ab8df3f7ef29154f010566857333108d98022ec20f3f29
- Size of remote file:
- 134 kB
- SHA256:
- 65d8c187c3cb1d9c7088669a71d469a38c13f349ee0aae00e9f43f31d50602a8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.