Instructions to use jialicheng/hubert-base-speech_commands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jialicheng/hubert-base-speech_commands with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="jialicheng/hubert-base-speech_commands")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("jialicheng/hubert-base-speech_commands") model = AutoModelForAudioClassification.from_pretrained("jialicheng/hubert-base-speech_commands", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f27429639da105a93c2d21fc5e950dd037a8b1921da506257c43691efb4303f2
- Size of remote file:
- 378 MB
- SHA256:
- a057942f07ad568c1f551fe91b9cf66ce52be420183309d7cfdc21c86b7c18e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.