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:
- 02c386ebca76a1eacef3319e2cb9dc9a482b76dcfd31f643e2299e2febee38bf
- Size of remote file:
- 148 kB
- SHA256:
- 5332a4400c78ff7066be6eb5200f74d446913ad2ac75ce803aef28da62f89464
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