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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9ccef49d4eaf778d4532432305d8d9234fc4575312576ce3fbcf6a9ce317c7c
- Size of remote file:
- 378 MB
- SHA256:
- 3c212d9d690f17df74376277849753949cd1210e04a0b97643b55c38fd5f7b67
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