Instructions to use mageec/wave2vec2_capstone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mageec/wave2vec2_capstone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mageec/wave2vec2_capstone")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("mageec/wave2vec2_capstone") model = AutoModelForAudioClassification.from_pretrained("mageec/wave2vec2_capstone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 594d9d9f2ddf10456200db0ee9cb626a5a595c52b0d5bcd124490fdc8582c04e
- Size of remote file:
- 4.73 kB
- SHA256:
- 3b344b694a1b3e8d578d4ba318f49c7995e53667f5dfe1b6b74dcfc56253d6b4
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