Instructions to use hf-internal-testing/tiny-random-WavLMForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WavLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-WavLMForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WavLMForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-WavLMForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0e7bb3c24774667c639b4b49792966e5b91ca2f93b5cae14f04c28d13c8ac8d
- Size of remote file:
- 296 kB
- SHA256:
- 9ba33a427eca051de87b07e9fa63d66b42d6c1fc1902e93ae2162db2f8e987ce
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