Instructions to use hf-tiny-model-private/tiny-random-WavLMForCTC 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-WavLMForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-WavLMForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForCTC") model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc06a9b2dd45abd1f23bf95ae8ae10b57a0bbda59f593d6483166054a74e2831
- Size of remote file:
- 143 kB
- SHA256:
- 4d2db1a99062e477ebb0c6e4046b7d5411ab63614c95205f081079f3c5e61a24
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