Instructions to use mazkooleg/0-9up-data2vec-audio-base-960h-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mazkooleg/0-9up-data2vec-audio-base-960h-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mazkooleg/0-9up-data2vec-audio-base-960h-ft")# Load model directly from transformers import AutoTokenizer, AutoModelForAudioClassification tokenizer = AutoTokenizer.from_pretrained("mazkooleg/0-9up-data2vec-audio-base-960h-ft") model = AutoModelForAudioClassification.from_pretrained("mazkooleg/0-9up-data2vec-audio-base-960h-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 863b86f87eb306eb252ed7167c871d9dd3c2e45f83c9e5f850d8a941b773ed8c
- Size of remote file:
- 374 MB
- SHA256:
- 2c4246ff930149ee1e87fcf23fabd6cd5b7f566c7a7d7ecd66b80b3e816a9746
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