Instructions to use mispeech/ced-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/ced-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/ced-small", trust_remote_code=True)# Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c27ee3ea7c2a095e26750f96487ed7e4914bb15763798e5405846611d3454ec
- Size of remote file:
- 22.8 MB
- SHA256:
- 18fa4fa30c1872c322c6b08f2824c9dd6f7fe149b8aa21320ddceb77007cff75
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