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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 355977f3522cecb66289734dbb184021290eb30c4928aa18f858f2accbf3826a
- Size of remote file:
- 134 kB
- SHA256:
- f3f10f54b2d63c07c547f2e438b9244c9e7c2d6b548dfedd3f1d9241070a6201
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.