Instructions to use galsenai/wav2vec2-base-waxal-keyword-spotting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use galsenai/wav2vec2-base-waxal-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="galsenai/wav2vec2-base-waxal-keyword-spotting")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("galsenai/wav2vec2-base-waxal-keyword-spotting") model = AutoModelForAudioClassification.from_pretrained("galsenai/wav2vec2-base-waxal-keyword-spotting") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3092c7aca5cf1203f3403be165510c5f587575fc62b68462a0fadf1d25932521
- Size of remote file:
- 378 MB
- SHA256:
- 8ff2f55293646a322bd841d96b0bb16c584160b4d0fa2ea7902ffe2ca60ea963
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