Instructions to use fkov/wav2vec2-base-ft-keyword-spotting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fkov/wav2vec2-base-ft-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="fkov/wav2vec2-base-ft-keyword-spotting")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("fkov/wav2vec2-base-ft-keyword-spotting") model = AutoModelForAudioClassification.from_pretrained("fkov/wav2vec2-base-ft-keyword-spotting", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b17678c15e5e6cfd0f84e56adc2256299f4bbab2cb64cc6c3c79540bddb4544f
- Size of remote file:
- 378 MB
- SHA256:
- dc976e3805435865806bfe38b5b498a6ad5eaefd135a4328984ea15f2c20a765
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