Instructions to use Kibalama/Speech_Commands_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/Speech_Commands_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Kibalama/Speech_Commands_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Kibalama/Speech_Commands_Model") model = AutoModelForAudioClassification.from_pretrained("Kibalama/Speech_Commands_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kibalama/Speech_Commands_Model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Kibalama/Speech_Commands_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://Kibalama/Speech_Commands_Model/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/Kibalama/Speech_Commands_Model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 5c95d3529738aa3c612f0fbf655acbb3b18da82811e34a02b6f9503bbd3ca7a8
- Size of remote file:
- 5.3 kB
- SHA256:
- 51742273e232767749d8fd1fc654f12033049ff81598861fbc4193e5598ba7a1
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