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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3370087c76c41678b6f95c1f21490732a4330a85deba8f0e3edaf565afc4d7c
- Size of remote file:
- 161 kB
- SHA256:
- 782f93ebf3f710bb529532c63d8a6ea5fb9aff0154bb8f945162246b7cea147a
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