Instructions to use mispeech/ced-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/ced-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/ced-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf51cfec968794658804ca3d8456e57b5523dd810273158186ce0536a0246103
- Size of remote file:
- 343 MB
- SHA256:
- 314935693ed1dcef07576ca0c41277c51c642f3847bc5eb03918c5277eb79af9
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