Instructions to use hf-tiny-model-private/tiny-random-WavLMForSequenceClassification 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-WavLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-tiny-model-private/tiny-random-WavLMForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c30de79e606a7762b6b27045ed7baba35d2b22c7203214c0992fd79835124a4e
- Size of remote file:
- 141 kB
- SHA256:
- 87740f13b01f8f3edff4c38261374a08bf9104fdafb7b82b717ec6a72930b21f
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