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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bf83abdd96ae36019ac3e30cae0ff5faecfe80f2914a6f2ef87919469b7c08c
- Size of remote file:
- 86.5 MB
- SHA256:
- a495e319a620dec4c8b7ca7bb927667ed68169a9efc1a943ccbf5d5c6f9c0c71
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