Instructions to use jialicheng/whisper-tiny-speech_commands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jialicheng/whisper-tiny-speech_commands with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="jialicheng/whisper-tiny-speech_commands")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("jialicheng/whisper-tiny-speech_commands") model = AutoModelForAudioClassification.from_pretrained("jialicheng/whisper-tiny-speech_commands", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cd8373f1f438dd71ae9f1f73d11f13caf2e55392d2e73789825be7b5fa00705
- Size of remote file:
- 5.11 kB
- SHA256:
- 734b5fa74bca8a553154a8842516df32fbe29da497c3bb2c0098c57b2454afd7
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