Instructions to use jialicheng/whisper-base-speech_commands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jialicheng/whisper-base-speech_commands with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="jialicheng/whisper-base-speech_commands")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("jialicheng/whisper-base-speech_commands") model = AutoModelForAudioClassification.from_pretrained("jialicheng/whisper-base-speech_commands") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ea835791489c437fe05cae854acca61b5f7f3c507cc11e4aa06eced4ca3f36b
- Size of remote file:
- 5.11 kB
- SHA256:
- 11e5f5374413173fb4a1b7e42ba0060c3d5a0137bb62d4241906640eb26e1678
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