Instructions to use anderloh/testV5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/testV5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/testV5")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/testV5") model = AutoModelForAudioClassification.from_pretrained("anderloh/testV5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3da66e8f8d91e7f77a2b7231f159354dbb4b41b595060cd9d756b190b2e7e55
- Size of remote file:
- 52.2 MB
- SHA256:
- 4a69333fbd2015de668219d7680d5afdd75d5de1576fa52e66fa501eff2fb145
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