Instructions to use mageec/wav2vec2_capstone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mageec/wav2vec2_capstone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mageec/wav2vec2_capstone")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("mageec/wav2vec2_capstone") model = AutoModelForAudioClassification.from_pretrained("mageec/wav2vec2_capstone") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a0ccb0aad7e0579f90576238e230d3493966abb87f23a93681b4114d1698a48
- Size of remote file:
- 4.73 kB
- SHA256:
- e4d1d5a3482e1306f153b5b6d9b33bee63d0e38f2d8e1c0d71b1d1d40ad15260
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