Instructions to use hf-internal-testing/tiny-random-WavLMForXVector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WavLMForXVector with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WavLMForXVector") model = AutoModelForAudioXVector.from_pretrained("hf-internal-testing/tiny-random-WavLMForXVector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c906a223496db989721e8982f37314d1b89601818957b79cefa01ba9169b00d
- Size of remote file:
- 162 kB
- SHA256:
- 72a360f7992e7f83cb4e8eb57fd93f93724ba4a52f53cf0c6a43db6b583f278f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.