Instructions to use hf-internal-testing/tiny-random-wavlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-wavlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-wavlm")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-wavlm") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-wavlm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2136c0223f886f3ca7ca1a21ec5ce33f07676229d06c36467063ee169735dcb9
- Size of remote file:
- 167 kB
- SHA256:
- 3011a742dd6656a66ddb868241e5de03e0eb651a18e7d30f7eab4507ab3597cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.