Instructions to use hf-tiny-model-private/tiny-random-WavLMModel 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-WavLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-WavLMModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-WavLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98c68ab04468c6741b566ae404c442a684e6d51d477d96df52b19a1cdebe87e6
- Size of remote file:
- 140 kB
- SHA256:
- a45cd3cb8180bb964c14119ca49b773a165995157ac0f10c28cc4eecf7921e43
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