Instructions to use Teapack1/model_KWS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Teapack1/model_KWS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Teapack1/model_KWS")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Teapack1/model_KWS") model = AutoModelForAudioClassification.from_pretrained("Teapack1/model_KWS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de46e956b4efda4d2bff5c47f6512d3c60f31cd29c8d191ea891c6f79a9a4938
- Size of remote file:
- 378 MB
- SHA256:
- 94421465708e2f629858513df6a8d44265a9a3bb01ba1a267ee579d5dc6bb767
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