Instructions to use mispeech/ced-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/ced-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/ced-mini", trust_remote_code=True)# Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-mini", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47260d74039717086f51522f8a41431733e5ca7c172ee8cc6c1a2d24caac2469
- Size of remote file:
- 38.8 MB
- SHA256:
- e4070d02ca53ec7a1df83bbf779a0e70bc2bbc0a9bacde6330e1dc37c990b215
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