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:
- c09128d566bc5c1039833009b849eb033d3250d5bc44ba94436179f1c5b1d85a
- Size of remote file:
- 378 MB
- SHA256:
- a1cdac9e20b24c0fd9b3a4e82d18a735b73494fdb44b95ad6fd201f8615ce2bb
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