Instructions to use hf-internal-testing/tiny-random-WavLMForAudioFrameClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WavLMForAudioFrameClassification with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WavLMForAudioFrameClassification") model = AutoModelForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-WavLMForAudioFrameClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a64c42b8cffe56a0f167d29c68ad59818b1ecc1192df0c2bc065c7f474e6b76
- Size of remote file:
- 121 kB
- SHA256:
- 2eea6bce06f80810b43d26af090278fefe68d828b489e1aeecc695f7409da460
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.