Instructions to use hf-tiny-model-private/tiny-random-WavLMForXVector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-WavLMForXVector with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForXVector") model = AutoModelForAudioXVector.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForXVector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adbb4b9420f156aecd917fa6040cf66e72b02a5be20c92182d47d40645cab0c7
- Size of remote file:
- 183 kB
- SHA256:
- 3b7dbb8e7f6faca8ec18764147d9684a7ff998e97ec3e66918e3f3b0b41bc40d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.